miχPODS: New baseline

TODO

• why does the stability diagram load multiple times

• Is there an error in ONI phase labeling for the newer runs, presumably SST changed some.

• Add diagnostic for convective “adjustment” or diff == 1

Introduction

Analysis of χpod shear mixing measurements recorded over approximately 15 years has revealed that mixing varies on multiple timescales, from hourly to interannual, in the tropical Pacific and Indian Oceans (Moum, 2021; Cherian et al., 2020b; Warner et al., 2016; Warner and Moum, 2019; Pujiana et al., 2018; Moum et al., 2009, 2013). These results show that equatorial mixing is organized in large-scale coherent patterns.

We pask whether these patterns, a decade-long observed mixing dataset (from χpods) to develop mixing process-oriented diagnostics (miχPODs) that assess the variability of parameterized equatorial mixing from monthly to decadal timescales; and its accumu- lated impact on model biases by applying miχPODs to a suite of existing CMIP6-class forced ocean and coupled model experiments from NCAR and GFDL.

Following Large and Gent (1999), the comparison of a 1D mixing model with LES or DNS is clean, because the forcing is the same, and direct, because the evaluation compares turbulence quantities (despite shortcomings such as model errors and idealized forcing). In contrast, the performance of a mixing scheme in global and regional model simulations is tested by comparing the simulated mean state to an observed mean state after a long integration (for e.g. Blanke and Delecluse, 1993; Robitaille and Weaver, 1995; Large and Gent, 1999; Gutjahr et al., 2020). This comparison is not clean, because of errors in forcing fields, nor is it direct, since mean state properties are not a direct output of the mixing scheme.

We propose diagnostics that evaluate the representation of the variability in parameterized equatorial mixing using the multiyear χpod turbulence dataset. While this comparison is not clean, it is direct, a substantial improvement to the current state-of-the-art approach.

These diagnostics are direct evaluations of the models’ ability to simulate the observed variability of ocean vertical mixing and reproduce large-scale patterns recorded in equatorial mixing observations.

Parameters

• TODO write down KPP and Jochum equations

• TODO something about total diffusivity field in model. Total diffusivity \(K = K_D + K_{KPP}\). and mapping from physical process to parameterization.

We perturb the following parameters

1. “Background mixing” parameters used to simulate mixing by unresolved processes such as breaking internal waves.

   1. A latitudinal varying background diffusivity \(K_D\) using the formulation by Jochum (2003)

   2. A latitudinal varying background viscosity \(K_V'\) derived from \(K_D\) using a Prandtl number of 5 (CHECK).

   3. A constant background viscosity \(K_V\) added to (2).

2. KPP Boundary layer scheme:

   1. Bulk Richardson number criterion \(Ri_c\) which controls the depth of the actively mixing layer.

3. KPP shear mixing scheme:

   1. Critical Richardson number \(Ri_0\) that controls when mixing occurs.

   2. Maximum shear mixing viscosity \(ν_0\)

Choices for the parameter changes were influenced by experiments matching 1D KPP simulations to the Large Eddy Simulations of Whitt et al (2022). The range of parameter values explored here is not exhaustive and is only intended to demonstrate the utility of these metrics in tuning and assessing model performance.

Datasets

Observations

1. TAO \(T, S, u, v\) and turbulence (Moum et al XXX)

• Note which depth χpods are being used

• Add a figure showing sampling and gaps

Model Simulations

The effect of these parameter changes are examined in a IPCC-class climate model: the development version of Community Earth System Model version 3 (CESM3), using the Modular Ocean Model version 6 (MOM6) ocean component, and the K Profile Paramerization vertical mixing scheme (Large et al., 1994).

For all simulations, we save a large number of variables every time step, which is an hour.

1. “old” baseline with KD=1e-5, KV=2e-4 (NCAR/MOM6#238)

2. old baseline + kpp.lmd.004 with KPP ν0=2.5, Ric=0.2, Ri0=0.5

3. new baseline : KD=0, KV=0

4. new baseline : kpp.lmd.004 with KPP ν0=2.5, Ric=0.2, Ri0=0.5

5. new baseline : kpp.lmd.005 with KPP ν0=2.5, Ric=0.3, Ri0=0.5

Summary

1. Turning down the background visc by a factor of 40, (2e-4 → 5e-5 m2/s)

   1. sharpens the EUC relative to TAO

   2. changes the stability diagram for El-Nino warming phase.

2. Using Ri_c=0.2, so shallower KPP surface layer, is key as Dan mentioned.

Setup

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%load_ext autoreload
%load_ext rich
%load_ext watermark

%xmode minimal

import math
import warnings
import os

import cf_xarray as cfxr
import dcpy.datatree  # noqa
import distributed
import holoviews as hv
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tqdm
from datatree import DataTree

import xarray as xr

%aimport pump
from holoviews.plotting.bokeh.util import select_legends

from pump import mixpods

hv.notebook_extension("bokeh")

cfxr.set_options(warn_on_missing_variables=False)
xr.set_options(keep_attrs=True, display_expand_data=False)

plt.style.use("bmh")
plt.rcParams["figure.dpi"] = 140

# https://github.com/dask/distributed/issues/8022
import warnings
import bokeh

# warnings.simplefilter("ignore", bokeh.util.warnings.BokehUserWarning)
import datatree


%watermark -iv

Show code cell output Hide code cell output

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload
Exception reporting mode: Minimal

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/dask_jobqueue/core.py:20: FutureWarning: tmpfile is deprecated and will be removed in a future release. Please use dask.utils.tmpfile instead.
  from distributed.utils import tmpfile

holoviews  : 1.17.0
distributed: 2023.8.0
dcpy       : 0.1.dev387+gd06c937
xarray     : 2023.7.0
pandas     : 2.0.3
numpy      : 1.24.4
bokeh      : 3.2.2
datatree   : 0.0.12
cf_xarray  : 0.8.4
tqdm       : 4.66.1
pump       : 1.0+273.g892024e.dirty
matplotlib : 3.7.1

Show code cell source Hide code cell source

if "client" in locals():
    client.close()  # noqa
    del client  # noqa
if "cluster" in locals():
    cluster.close()  # noqa

import ncar_jobqueue

cluster = ncar_jobqueue.NCARCluster(
    local_directory="/local_scratch/pbs.$PBS_JOBID/dask/spill",
)
cluster.adapt(minimum_jobs=2, maximum_jobs=8)
client = distributed.Client(cluster)
client

Show code cell output Hide code cell output

Client

Client-500a231d-3b92-11ee-a7a2-3cecef1b1260

Connection method: Cluster object	Cluster type: dask_jobqueue.PBSCluster
Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/casper/proxy/8787/status

Launch dashboard in JupyterLab

Cluster Info

PBSCluster

bd2779a5

Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/casper/proxy/8787/status	Workers: 0
Total threads: 0	Total memory: 0 B

Scheduler Info

Scheduler

Scheduler-ffb8a918-2424-4fa1-ae87-90e7079370d9

Comm: tcp://10.12.206.31:36719	Workers: 0
Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/dcherian/casper/proxy/8787/status	Total threads: 0
Started: Just now	Total memory: 0 B

Workers

Read

MOM6

Look at catalog

The catalog only contains surface variables, full depth variables, NO SECTIONS YET.

catalog = pump.catalog.open_mom6_catalog()
catalog

pump-mom6-catalog catalog with 41 dataset(s) from 41 asset(s):

	unique
casename	11
stream	5
path	41
baseline	2
levels	2
frequency	3
variables	85
shortname	11
description	9
derived_variables	0

catalog.df["shortname"].unique()

array(['baseline', 'kpp.lmd.004', 'baseline.001', 'baseline.epbl.001',
       'baseline.hb', 'baseline.kpp.lmd.002', 'baseline.kpp.lmd.003',
       'baseline.kpp.lmd.004', 'new_baseline.hb',
       'new_baseline.kpp.lmd.004', 'new_baseline.kpp.lmd.005'],
      dtype=object)

Subset catalog

Note

Use baseline.hb instead of baseline.001. This was run by Anna-Lena, and does not have gaps, and has heat budget output.

catalog_sub = pd.concat(
    [
        cat.df
        for cat in [
            # pick two "old" baseline simulations
            catalog.search(
                stream="combined",
                levels=65,
                shortname=["baseline.hb", "baseline.kpp.lmd.004"],
            ),
            # and all new baseline simulations
            catalog.search(stream="combined", levels=65, baseline="new"),
        ]
    ]
)

with pd.option_context("display.max_colwidth", None):
    display(catalog_sub[["shortname", "casename", "path"]])

	shortname	casename	path
0	baseline.hb	gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.hb	/glade/campaign/cgd/oce/projects/pump/cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.hb/run/jsons/combined.json
1	baseline.kpp.lmd.004	gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods	/glade/campaign/cgd/oce/projects/pump/cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods/run/jsons/combined.json
0	new_baseline.hb	gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb	/glade/campaign/cgd/oce/projects/pump/cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb/run/jsons/combined.json
1	new_baseline.kpp.lmd.004	gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods	/glade/campaign/cgd/oce/projects/pump/cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods/run/jsons/combined.json
2	new_baseline.kpp.lmd.005	gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods	/glade/campaign/cgd/oce/projects/pump/cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods/run/jsons/combined.json

backup

This is what I was doing before switching to catalog.search

catalog_sub = {
    k: catalog[k]
    for k in catalog.keys()
    if k == "kpp.lmd.004" or ("baseline" in k and "150" not in k)
}
display(catalog_sub)

{
    'baseline': ('baseline', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.001.mixpods'),
    'kpp.lmd.004': (
        'KPP ν0=2.5, Ric=0.2, Ri0=0.5',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N65.baseline.kpp.lmd.004.mixpods'
    ),
    'new_baseline.hb': ('KD=0, KV=0', 'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.hb'),
    'new_baseline.kpp.lmd.004': (
        'KPP ν0=2.5, Ric=0.2, Ri0=0.5',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods'
    ),
    'new_baseline.kpp.lmd.005': (
        'KPP ν0=2.5, Ri0=0.5',
        'gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods'
    )
}

Since there are no sections in the catalog, and we do a bunch of custom things, loop over entries and load the section output.

%autoreload

datasets = {}
with warnings.catch_warnings():
    warnings.simplefilter("ignore", category=UserWarning)
    warnings.simplefilter("ignore", category=FutureWarning)
    for _, (casename, shortname, description) in tqdm.tqdm(
        catalog_sub[["casename", "shortname", "description"]].iterrows()
    ):
        datasets.update(
            {
                shortname: mixpods.load_mom6_sections(casename).assign_attrs(
                    {"title": description}
                )
            }
        )

0it [00:00, ?it/s]

using new oni

1it [00:02,  2.34s/it]

using new oni

2it [00:04,  2.15s/it]

using new oni

3it [00:06,  2.09s/it]

using new oni

4it [00:08,  1.97s/it]

using new oni

5it [00:09,  1.99s/it]

tree = DataTree.from_dict(datasets)
tree

<xarray.DatasetView>
Dimensions:  ()
Data variables:
    *empty*

datatree.DataTree

• Groups: (5)
  <xarray.DatasetView>
  Dimensions:                         (time: 534360, zl: 37, zi: 37, nv: 2)
  Coordinates: (12/16)
    * nv                              (nv) float64 1.0 2.0
    * time                            (time) datetime64[ns] 1958-01-01T00:30:00...
      xh                              float64 -140.0
      yh                              float64 0.0625
      yq                              float64 -0.0625
    * zi                              (zi) float64 -523.8 -481.0 ... -2.5 -0.0
      ...                              ...
      oni                             (time) float32 1.66 1.66 1.66 ... 0.81 0.81
      en_mask                         (time) bool True True True ... True True
      ln_mask                         (time) bool False False ... False False
      warm_mask                       (time) bool True True True ... True True
      cool_mask                       (time) bool False False ... False False
      enso_transition                 (time) <U12 'El-Nino warm' ... 'El-Nino w...
  Data variables: (12/58)
      KPP_BulkRi                      (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_N2                          (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLT_temp_budget             (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLtransport_heat            (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_OBLdepth                    (time) float32 dask.array<chunksize=(534360,), meta=np.ndarray>
      KPP_buoyFlux                    (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      ...                              ...
      ν                               (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      shear_prod                      (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      eps                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      chi                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      Rif                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      sst                             (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    baseline

  baseline.hb

  • Groups: (0)
  • Dimensions:
    • time: 534360
    • zl: 37
    • zi: 37
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      1958-01-01T00:30:00 ... 2018-12-...

      array(['1958-01-01T00:30:00.000000000', '1958-01-01T01:30:00.000000000',
             '1958-01-01T02:30:00.000000000', ..., '2018-12-31T21:30:00.000000000',
             '2018-12-31T22:30:00.000000000', '2018-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -523.8 -481.0 -442.5 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22,
             -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57,
             -133.72, -120.66, -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,
              -49.94,  -42.66,  -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,
              -10.  ,   -7.5 ,   -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -547.8 -502.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385,
             -307.605, -283.72 , -261.51 , -240.79 , -221.4  , -203.215, -186.13 ,
             -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6  ,  -91.425,
              -80.98 ,  -71.255,  -62.24 ,  -53.925,  -46.3  ,  -39.355,  -33.075,
              -27.455,  -22.485,  -18.155,  -14.46 ,  -11.385,   -8.75 ,   -6.25 ,
               -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(37, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 18.86 MiB 316.52 kiB Shape (37, 534360) (37, 8760) Dask graph 61 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      1.66 1.66 1.66 ... 0.81 0.81 0.81

      array([1.66, 1.66, 1.66, ..., 0.81, 0.81, 0.81], dtype=float32)

    • en_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • ln_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      'El-Nino warm' ... 'El-Nino warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['El-Nino warm', 'El-Nino warm', 'El-Nino warm', ...,
             'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')

  • Data variables: (58)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLT_temp_budget
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat content change due to non-local transport, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_kheat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Heat diffusivity due to KPP, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • S2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of vertical shear used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • T_advection_xy
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Horizontal convergence of residual mean advective fluxes of heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • T_lbdxy_cont_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Lateral diffusion tracer content tendency for T

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Th_tendency_vert_remap
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Vertical remapping tracer content tendency for Heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • boundary_forcing_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Boundary forcing heat tendency

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • frazil_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat tendency due to frazil formation

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • opottempdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_dianeutral_mixing

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • opottemppmdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_mesoscale_neutral_diffusion

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • opottemptend
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of Sea Water Potential Temperature Expressed as Heat Content

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 150.84 MiB 2.47 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 6 graph layers Data type float64 numpy.ndarray

    • ν
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $ε$

      units :

      W/kg

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 2.04 MiB 34.22 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    baseline

  <xarray.DatasetView>
  Dimensions:                         (time: 218424, zi: 37, zl: 37, nv: 2)
  Coordinates: (12/16)
    * nv                              (nv) float64 1.0 2.0
    * time                            (time) datetime64[ns] 2003-01-07T00:30:00...
      xh                              float64 -140.0
      yh                              float64 0.0625
      yq                              float64 -0.0625
    * zi                              (zi) float64 -523.8 -481.0 ... -2.5 -0.0
      ...                              ...
      oni                             (time) float32 0.63 0.63 0.63 ... nan nan
      en_mask                         (time) bool False False ... False False
      ln_mask                         (time) bool False False ... False False
      warm_mask                       (time) bool True True True ... True True
      cool_mask                       (time) bool False False ... False False
      enso_transition                 (time) <U12 '____________' ... '_________...
  Data variables: (12/54)
      KPP_N2                          (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLT_temp_budget             (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLtransport_heat            (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_OBLdepth                    (time) float32 dask.array<chunksize=(218424,), meta=np.ndarray>
      KPP_buoyFlux                    (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_kheat                       (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      ...                              ...
      ν                               (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      shear_prod                      (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      eps                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      chi                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      Rif                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      sst                             (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  baseline.kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • time: 218424
    • zi: 37
    • zl: 37
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      cartesian_axis :

      N

      long_name :

      vertex number

      units :

      none

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-07T00:30:00 ... 2028-01-...

      array(['2003-01-07T00:30:00.000000000', '2003-01-07T01:30:00.000000000',
             '2003-01-07T02:30:00.000000000', ..., '2028-01-06T21:30:00.000000000',
             '2028-01-06T22:30:00.000000000', '2028-01-06T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -523.8 -481.0 -442.5 ... -2.5 -0.0

      cartesian_axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22,
             -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57,
             -133.72, -120.66, -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,
              -49.94,  -42.66,  -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,
              -10.  ,   -7.5 ,   -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -547.8 -502.4 ... -3.75 -1.25

      cartesian_axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385,
             -307.605, -283.72 , -261.51 , -240.79 , -221.4  , -203.215, -186.13 ,
             -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6  ,  -91.425,
              -80.98 ,  -71.255,  -62.24 ,  -53.925,  -46.3  ,  -39.355,  -33.075,
              -27.455,  -22.485,  -18.155,  -14.46 ,  -11.385,   -8.75 ,   -6.25 ,
               -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.67 MiB 68.44 kiB Shape (218424,) (8760,) Dask graph 25 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.67 MiB 68.44 kiB Shape (218424,) (8760,) Dask graph 25 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(37, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 7.71 MiB 316.52 kiB Shape (37, 218424) (37, 8760) Dask graph 25 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      0.63 0.63 0.63 0.63 ... nan nan nan

      array([0.63, 0.63, 0.63, ...,  nan,  nan,  nan], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... '____________'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             '____________', '____________', '____________'], dtype='<U12')

  • Data variables: (54)
    • KPP_N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLT_temp_budget
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat content change due to non-local transport, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_kheat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Heat diffusivity due to KPP, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • S2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of vertical shear used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • T_advection_xy
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Horizontal convergence of residual mean advective fluxes of heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • T_lbdxy_cont_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Lateral diffusion tracer content tendency for T

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      standard_name :

      ocean_vertical_diffusive_heat_flux

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Th_tendency_vert_remap
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Vertical remapping tracer content tendency for Heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • boundary_forcing_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Boundary forcing heat tendency

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • frazil_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat tendency due to frazil formation

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • opottempdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_dianeutral_mixing

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • opottemppmdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_mesoscale_neutral_diffusion

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • opottemptend
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of Sea Water Potential Temperature Expressed as Heat Content

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(218424,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 853.22 kiB 853.22 kiB Shape (218424,) (218424,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 61.66 MiB 2.47 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 6 graph layers Data type float64 numpy.ndarray

    • ν
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 30.83 MiB 1.24 MiB Shape (218424, 37) (8760, 37) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $ε$

      units :

      W/kg

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 30.83 MiB 1.20 MiB Shape (218424, 37) (8760, 36) Dask graph 50 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 853.22 kiB 34.22 kiB Shape (218424,) (8760,) Dask graph 25 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:               (time: 534360, zl: 37, zi: 37, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 1958-01-01T00:30:00 ... 2018-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 1.66 1.66 1.66 1.66 ... 0.81 0.81 0.81
      en_mask               (time) bool True True True True ... True True True
      ln_mask               (time) bool False False False ... False False False
      warm_mask             (time) bool True True True True ... True True True
      cool_mask             (time) bool False False False ... False False False
      enso_transition       (time) <U12 'El-Nino warm' ... 'El-Nino warm'
  Data variables: (12/49)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(534360,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(534360,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      ...                    ...
      ν                     (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KD=0, KV=0

  new_baseline.hb

  • Groups: (0)
  • Dimensions:
    • time: 534360
    • zl: 37
    • zi: 37
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      1958-01-01T00:30:00 ... 2018-12-...

      array(['1958-01-01T00:30:00.000000000', '1958-01-01T01:30:00.000000000',
             '1958-01-01T02:30:00.000000000', ..., '2018-12-31T21:30:00.000000000',
             '2018-12-31T22:30:00.000000000', '2018-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -523.8 -481.0 -442.5 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22,
             -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57,
             -133.72, -120.66, -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,
              -49.94,  -42.66,  -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,
              -10.  ,   -7.5 ,   -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -547.8 -502.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385,
             -307.605, -283.72 , -261.51 , -240.79 , -221.4  , -203.215, -186.13 ,
             -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6  ,  -91.425,
              -80.98 ,  -71.255,  -62.24 ,  -53.925,  -46.3  ,  -39.355,  -33.075,
              -27.455,  -22.485,  -18.155,  -14.46 ,  -11.385,   -8.75 ,   -6.25 ,
               -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 4.08 MiB 68.44 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(37, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 18.86 MiB 316.52 kiB Shape (37, 534360) (37, 8760) Dask graph 61 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      1.66 1.66 1.66 ... 0.81 0.81 0.81

      array([1.66, 1.66, 1.66, ..., 0.81, 0.81, 0.81], dtype=float32)

    • en_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • ln_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      'El-Nino warm' ... 'El-Nino warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['El-Nino warm', 'El-Nino warm', 'El-Nino warm', ...,
             'El-Nino warm', 'El-Nino warm', 'El-Nino warm'], dtype='<U12')

  • Data variables: (49)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(534360,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 2.04 MiB 2.04 MiB Shape (534360,) (534360,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 150.84 MiB 2.47 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 6 graph layers Data type float64 numpy.ndarray

    • ν
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.24 MiB Shape (534360, 37) (8760, 37) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $ε$

      units :

      W/kg

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 75.42 MiB 1.20 MiB Shape (534360, 37) (8760, 36) Dask graph 122 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 2.04 MiB 34.22 kiB Shape (534360,) (8760,) Dask graph 61 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KD=0, KV=0

  <xarray.DatasetView>
  Dimensions:               (time: 131400, zl: 37, zi: 37, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 2003-01-01T00:30:00 ... 2017-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 0.63 0.63 0.63 ... -0.87 -0.87 -0.87
      en_mask               (time) bool False False False ... False False False
      ln_mask               (time) bool False False False False ... True True True
      warm_mask             (time) bool True True True True ... True True True
      cool_mask             (time) bool False False False ... False False False
      enso_transition       (time) <U12 '____________' ... 'La-Nina warm'
  Data variables: (12/49)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      ...                    ...
      ν                     (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  new_baseline.kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • time: 131400
    • zl: 37
    • zi: 37
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-01T00:30:00 ... 2017-12-...

      array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000',
             '2003-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000',
             '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -523.8 -481.0 -442.5 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22,
             -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57,
             -133.72, -120.66, -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,
              -49.94,  -42.66,  -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,
              -10.  ,   -7.5 ,   -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -547.8 -502.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385,
             -307.605, -283.72 , -261.51 , -240.79 , -221.4  , -203.215, -186.13 ,
             -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6  ,  -91.425,
              -80.98 ,  -71.255,  -62.24 ,  -53.925,  -46.3  ,  -39.355,  -33.075,
              -27.455,  -22.485,  -18.155,  -14.46 ,  -11.385,   -8.75 ,   -6.25 ,
               -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(37, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 4.64 MiB 316.52 kiB Shape (37, 131400) (37, 8760) Dask graph 15 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      0.63 0.63 0.63 ... -0.87 -0.87

      array([ 0.63,  0.63,  0.63, ..., -0.87, -0.87, -0.87], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... 'La-Nina warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')

  • Data variables: (49)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float64 numpy.ndarray

    • ν
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $ε$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:               (time: 131400, zl: 37, zi: 37, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 2003-01-01T00:30:00 ... 2017-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 0.63 0.63 0.63 ... -0.87 -0.87 -0.87
      en_mask               (time) bool False False False ... False False False
      ln_mask               (time) bool False False False False ... True True True
      warm_mask             (time) bool True True True True ... True True True
      cool_mask             (time) bool False False False ... False False False
      enso_transition       (time) <U12 '____________' ... 'La-Nina warm'
  Data variables: (12/49)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      ...                    ...
      ν                     (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ri0=0.5

  new_baseline.kpp.lmd.005

  • Groups: (0)
  • Dimensions:
    • time: 131400
    • zl: 37
    • zi: 37
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-01T00:30:00 ... 2017-12-...

      array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000',
             '2003-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000',
             '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -523.8 -481.0 -442.5 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22,
             -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57,
             -133.72, -120.66, -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,
              -49.94,  -42.66,  -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,
              -10.  ,   -7.5 ,   -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -547.8 -502.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385,
             -307.605, -283.72 , -261.51 , -240.79 , -221.4  , -203.215, -186.13 ,
             -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6  ,  -91.425,
              -80.98 ,  -71.255,  -62.24 ,  -53.925,  -46.3  ,  -39.355,  -33.075,
              -27.455,  -22.485,  -18.155,  -14.46 ,  -11.385,   -8.75 ,   -6.25 ,
               -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(37, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 4.64 MiB 316.52 kiB Shape (37, 131400) (37, 8760) Dask graph 15 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      0.63 0.63 0.63 ... -0.87 -0.87

      array([ 0.63,  0.63,  0.63, ..., -0.87, -0.87, -0.87], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... 'La-Nina warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')

  • Data variables: (49)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float64 numpy.ndarray

    • ν
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $ε$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ri0=0.5

• Dimensions:
• Coordinates: (0)
• Data variables: (0)
• Attributes: (0)

Reprocess 004, 005 TO DELETE

from pump import mixpods
from mom6_tools.kerchunk import (
    combine_stream_jsons_as_groups,
    generate_references_for_stream,
)

for casename in [
    "gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods",
    "gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods",
]:
    caseroot = f"{mixpods.ROOT}/cesm/{casename}"
    print(caseroot)

    for stream in ["h", "hm", "hm.wci", "sfc"]:
        generate_references_for_stream(
            caseroot=caseroot,
            stream=stream,
            missing_stream="warn",
            existing_output="overwrite",
        )

    combine_stream_jsons_as_groups(caseroot=caseroot)

import pump

pump.catalog.build_mom6_catalog()

/glade/campaign/cgd/oce/projects/pump//cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods

/glade/u/home/dcherian/python/mom6-tools/mom6_tools/kerchunk.py:104: RuntimeWarning: No files found for caseroot: /glade/campaign/cgd/oce/projects/pump//cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods, stream: hm.wci
  warnings.warn(f"No files found for caseroot: {caseroot}, stream: {stream}", RuntimeWarning)

/glade/campaign/cgd/oce/projects/pump//cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods

/glade/u/home/dcherian/python/mom6-tools/mom6_tools/kerchunk.py:104: RuntimeWarning: No files found for caseroot: /glade/campaign/cgd/oce/projects/pump//cesm/gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods, stream: hm.wci
  warnings.warn(f"No files found for caseroot: {caseroot}, stream: {stream}", RuntimeWarning)

Successfully wrote ESM catalog json file to: file:///glade/campaign/cgd/oce/projects/pump/catalogs/pump-mom6-catalog.json

for casename in [
    "gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.004.mixpods",
    "gmom.e23.GJRAv3.TL319_t061_zstar_N65.new_baseline.kpp.lmd.005.mixpods",
]:
    mixpods.mom6_sections_to_zarr(casename)

100%|██████████| 33/33 [00:18<00:00,  1.75it/s]
100%|██████████| 33/33 [00:19<00:00,  1.68it/s]

Add TAO

tao_gridded = mixpods.load_tao()
tree["TAO"] = DataTree(tao_gridded)
tree = tree.dc.reorder_nodes(["TAO", ...])

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:265: UserWarning: The specified chunks separate the stored chunks along dimension "depth" starting at index 42. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:265: UserWarning: The specified chunks separate the stored chunks along dimension "time" starting at index 199726. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/dataset.py:265: UserWarning: The specified chunks separate the stored chunks along dimension "longitude" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.
  warnings.warn(

Add LES, microstructure

Check SST time series

sst = (
    xr.Dataset(
        {
            casename: node["sst"].reset_coords(drop=True)
            for casename, node in tree.children.items()
        }
    )
    .to_array("case")
    .load()
)

sst.rename("sst").resample(time="D").mean().hvplot.line(muted_alpha=0, by="case")

Post-process catalog subset

Slice

tree = tree.sel(time=slice("2003", "2017"))
tree

<xarray.DatasetView>
Dimensions:  ()
Data variables:
    *empty*

datatree.DataTree

• Groups: (6)
  <xarray.DatasetView>
  Dimensions:             (time: 131496, depth: 61, depthchi: 6)
  Coordinates: (12/19)
      deepest             (time) float64 dask.array<chunksize=(131496,), meta=np.ndarray>
    * depth               (depth) float64 -300.0 -295.0 -290.0 ... -10.0 -5.0 0.0
      eucmax              (time) float64 dask.array<chunksize=(71762,), meta=np.ndarray>
      latitude            float32 0.0
      longitude           float32 -140.0
      mld                 (time) float64 dask.array<chunksize=(131496,), meta=np.ndarray>
      ...                  ...
      oni                 (time) float32 0.92 0.92 0.92 0.92 ... -0.87 -0.87 -0.87
      en_mask             (time) bool True True True True ... False False False
      ln_mask             (time) bool False False False False ... True True True
      warm_mask           (time) bool False False False False ... True True True
      cool_mask           (time) bool True True True True ... False False False
      enso_transition     (time) <U12 'El-Nino cool' ... 'La-Nina warm'
  Data variables: (12/39)
      N2                  (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray>
      N2T                 (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray>
      Ri                  (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray>
      Rig_T               (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray>
      S                   (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray>
      S2                  (time, depth) float32 dask.array<chunksize=(71762, 61), meta=np.ndarray>
      ...                  ...
      Rig                 (time, depth) float64 dask.array<chunksize=(71762, 61), meta=np.ndarray>
      sst                 (time) float64 dask.array<chunksize=(71762,), meta=np.ndarray>
      Tflx_dia_diff       (time, depthchi) float64 nan nan nan nan ... nan nan nan
      ν                   (time, depthchi) float64 dask.array<chunksize=(71762, 6), meta=np.ndarray>
      Rif                 (time, depthchi) float64 dask.array<chunksize=(71762, 6), meta=np.ndarray>
      Jb                  (time, depthchi) float64 dask.array<chunksize=(71762, 6), meta=np.ndarray>
  Attributes:
      CREATION_DATE:                23:26 24-FEB-2021
      Data_Source:                  Global Tropical Moored Buoy Array Project O...
      File_info:                    Contact: Dai.C.McClurg@noaa.gov
      Request_for_acknowledgement:  If you use these data in publications or pr...
      _FillValue:                   1.0000000409184788e+35
      array:                        TAO/TRITON
      missing_value:                1.0000000409184788e+35
      platform_code:                0n165e
      site_code:                    0n165e
      wmo_platform_code:            52321

  TAO

  • Groups: (0)
  • Dimensions:
    • time: 131496
    • depth: 61
    • depthchi: 6
  • Coordinates: (19)
    • deepest
      (time)
      float64
      dask.array<chunksize=(131496,), meta=np.ndarray>

      description :

      Deepest depth with a valid observation

      units :

      m

      Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

    • depth
      (depth)
      float64
      -300.0 -295.0 -290.0 ... -5.0 0.0

      axis :

      Z

      positive :

      up

      units :

      m

      array([-300., -295., -290., -285., -280., -275., -270., -265., -260., -255.,
             -250., -245., -240., -235., -230., -225., -220., -215., -210., -205.,
             -200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
             -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
             -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
              -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
                0.])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(71762,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.00 MiB 560.64 kiB Shape (131496,) (71762,) Dask graph 2 chunks in 18 graph layers Data type float64 numpy.ndarray

    • latitude
      ()
      float32
      0.0

      array(0., dtype=float32)

    • longitude
      ()
      float32
      -140.0

      array(-140., dtype=float32)

    • mld
      (time)
      float64
      dask.array<chunksize=(131496,), meta=np.ndarray>

      long_name :

      $z_{MLD}$

      units :

      m

      Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(71762,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.00 MiB 560.64 kiB Shape (131496,) (71762,) Dask graph 2 chunks in 21 graph layers Data type float64 numpy.ndarray

    • reference_pressure
      ()
      int64
      0

      array(0)

    • shallowest
      (time)
      float64
      dask.array<chunksize=(131496,), meta=np.ndarray>

      Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

    • time
      (time)
      datetime64[ns]
      2003-01-01 ... 2017-12-31T23:00:00

      array(['2003-01-01T00:00:00.000000000', '2003-01-01T01:00:00.000000000',
             '2003-01-01T02:00:00.000000000', ..., '2017-12-31T21:00:00.000000000',
             '2017-12-31T22:00:00.000000000', '2017-12-31T23:00:00.000000000'],
            dtype='datetime64[ns]')

    • zeuc
      (depth, time)
      float64
      dask.array<chunksize=(42, 71488), meta=np.ndarray>

      Array Chunk Bytes 61.20 MiB 22.91 MiB Shape (61, 131496) (42, 71488) Dask graph 4 chunks in 4 graph layers Data type float64 numpy.ndarray

    • depthchi
      (depthchi)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      axis :

      Z

      positive :

      up

      units :

      m

      array([-89., -69., -59., -49., -39., -29.])

    • dcl_mask
      (depth, time)
      bool
      dask.array<chunksize=(61, 71762), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 7.65 MiB 4.17 MiB Shape (61, 131496) (61, 71762) Dask graph 2 chunks in 46 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      0.92 0.92 0.92 ... -0.87 -0.87

      array([ 0.92,  0.92,  0.92, ..., -0.87, -0.87, -0.87], dtype=float32)

    • en_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • warm_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      'El-Nino cool' ... 'La-Nina warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['El-Nino cool', 'El-Nino cool', 'El-Nino cool', ...,
             'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')

  • Data variables: (39)
    • N2
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      long_name :

      $N^2$

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • N2T
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      long_name :

      $N^2_T$

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • Ri
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      long_name :

      $Ri_g$

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • Rig_T
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 8 graph layers Data type float64 numpy.ndarray

    • S
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      41

      generic_name :

      sal

      long_name :

      SALINITY (PSU)

      name :

      S

      standard_name :

      sea_water_salinity

      units :

      PSU

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • S2
      (time, depth)
      float32
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      long_name :

      $S^2$

      Array Chunk Bytes 30.60 MiB 16.70 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray

    • T
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      FORTRAN_format :

      f10.2

      epic_code :

      20

      generic_name :

      temp

      long_name :

      TEMPERATURE (C)

      name :

      T

      standard_name :

      sea_water_temperature

      units :

      C

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • dens
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      long_name :

      $ρ$

      standard_name :

      sea_water_potential_density

      units :

      kg/m3

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • densT
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      description :

      density using T, S

      long_name :

      $ρ_T$

      standard_name :

      sea_water_potential_density

      units :

      kg/m3

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • lwnet
      (time)
      float32
      dask.array<chunksize=(131496,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1136

      generic_name :

      qln

      long_name :

      NET LONGWAVE RADIATION

      name :

      LWN

      units :

      W m-2

      Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • qlat
      (time)
      float32
      dask.array<chunksize=(131496,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      137

      generic_name :

      qlat

      long_name :

      LATENT HEAT FLUX

      name :

      QL

      units :

      W m-2

      Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • qnet
      (time)
      float32
      dask.array<chunksize=(131496,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      210

      generic_name :

      qtot

      long_name :

      TOTAL HEAT FLUX

      name :

      QT

      units :

      W/M**2

      standard_name :

      surface_downward_heat_flux_in_sea_water

      Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • qsen
      (time)
      float32
      dask.array<chunksize=(131496,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      138

      generic_name :

      qsen

      long_name :

      SENSIBLE HEAT FLUX

      name :

      QS

      units :

      W m-2

      Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • swnet
      (time)
      float32
      dask.array<chunksize=(131496,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1495

      generic_name :

      sw

      long_name :

      NET SHORTWAVE RADIATION

      name :

      SWN

      units :

      W/M**2

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float64
      dask.array<chunksize=(131496,), meta=np.ndarray>

      Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray

    • taux
      (time)
      float64
      dask.array<chunksize=(131496,), meta=np.ndarray>

      standard_name :

      surface_downward_x_stress

      Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

    • tauy
      (time)
      float64
      dask.array<chunksize=(131496,), meta=np.ndarray>

      standard_name :

      surface_downward_y_stress

      Array Chunk Bytes 1.00 MiB 1.00 MiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

    • theta
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      description :

      potential temperature using T, S=35

      long_name :

      $θ$

      standard_name :

      sea_water_potential_temperature

      units :

      degC

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float64 numpy.ndarray

    • u
      (time, depth)
      float32
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1205

      generic_name :

      u

      long_name :

      u

      name :

      u

      standard_name :

      sea_water_x_velocity

      units :

      m/s

      Array Chunk Bytes 30.60 MiB 16.70 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray

    • v
      (time, depth)
      float32
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      1206

      generic_name :

      v

      long_name :

      v

      name :

      v

      standard_name :

      sea_water_y_velocity

      units :

      m/s

      Array Chunk Bytes 30.60 MiB 16.70 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 4 graph layers Data type float32 numpy.ndarray

    • wind_dir
      (time)
      float32
      dask.array<chunksize=(131496,), meta=np.ndarray>

      FORTRAN_format :

      epic_code :

      410

      generic_name :

      long_name :

      WIND DIRECTION

      name :

      WD

      standard_name :

      wind_from_direction

      units :

      degrees

      Array Chunk Bytes 513.66 kiB 513.66 kiB Shape (131496,) (131496,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • pressure
      (depth)
      float64
      301.9 296.8 291.8 ... 5.028 -0.0

      standard_name :

      sea_water_pressure

      units :

      dbar

      array([301.87732362, 296.84242473, 291.80764803, 286.77299352,
             281.73846121, 276.70405112, 271.66976325, 266.63559761,
             261.60155422, 256.56763308, 251.5338342 , 246.5001576 ,
             241.46660329, 236.43317126, 231.39986155, 226.36667414,
             221.33360906, 216.30066632, 211.26784592, 206.23514788,
             201.2025722 , 196.17011889, 191.13778797, 186.10557945,
             181.07349333, 176.04152963, 171.00968835, 165.97796951,
             160.94637311, 155.91489917, 150.8835477 , 145.8523187 ,
             140.82121218, 135.79022817, 130.75936665, 125.72862766,
             120.69801119, 115.66751726, 110.63714587, 105.60689704,
             100.57677078,  95.54676709,  90.51688599,  85.48712749,
              80.4574916 ,  75.42797832,  70.39858766,  65.36931965,
              60.34017428,  55.31115157,  50.28225153,  45.25347416,
              40.22481948,  35.1962875 ,  30.16787822,  25.13959167,
              20.11142784,  15.08338675,  10.0554684 ,   5.02767282,
              -0.        ])

    • SA
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      standard_name :

      sea_water_absolute_salinity

      units :

      g/kg

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 6 graph layers Data type float64 numpy.ndarray

    • CT
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      standard_name :

      sea_water_conservative_temperature

      units :

      degC

      reference_scale :

      ITS-90

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 10 graph layers Data type float64 numpy.ndarray

    • α
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      units :

      1/K

      standard_name :

      sea_water_thermal_expansion_coefficient

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 11 graph layers Data type float64 numpy.ndarray

    • β
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      units :

      kg/g

      standard_name :

      sea_water_haline_contraction_coefficient

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 11 graph layers Data type float64 numpy.ndarray

    • Tz
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      ℃/m

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray

    • Sz
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      g/kg/m

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray

    • chi
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • KT
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      ocean_vertical_heat_diffusivity

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • eps
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • Jq
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • shred2
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 9 graph layers Data type float64 numpy.ndarray

    • Rig
      (time, depth)
      float64
      dask.array<chunksize=(71762, 61), meta=np.ndarray>

      long_name :

      $Ri^g$

      Array Chunk Bytes 61.20 MiB 33.40 MiB Shape (131496, 61) (71762, 61) Dask graph 2 chunks in 8 graph layers Data type float64 numpy.ndarray

    • sst
      (time)
      float64
      dask.array<chunksize=(71762,), meta=np.ndarray>

      description :

      potential temperature using T, S=35

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      units :

      degC

      Array Chunk Bytes 1.00 MiB 560.64 kiB Shape (131496,) (71762,) Dask graph 2 chunks in 5 graph layers Data type float64 numpy.ndarray

    • Tflx_dia_diff
      (time, depthchi)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      ocean_vertical_diffusive_heat_flux

      array([[nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             ...,
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan],
             [nan, nan, nan, nan, nan, nan]])

    • ν
      (time, depthchi)
      float64
      dask.array<chunksize=(71762, 6), meta=np.ndarray>

      standard_name :

      ocean_vertical_momentum_diffusivity

      Array Chunk Bytes 6.02 MiB 3.29 MiB Shape (131496, 6) (71762, 6) Dask graph 2 chunks in 13 graph layers Data type float64 numpy.ndarray

    • Rif
      (time, depthchi)
      float64
      dask.array<chunksize=(71762, 6), meta=np.ndarray>

      standard_name :

      flux_richardson_number

      Array Chunk Bytes 6.02 MiB 3.29 MiB Shape (131496, 6) (71762, 6) Dask graph 2 chunks in 53 graph layers Data type float64 numpy.ndarray

    • Jb
      (time, depthchi)
      float64
      dask.array<chunksize=(71762, 6), meta=np.ndarray>

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      Array Chunk Bytes 6.02 MiB 3.29 MiB Shape (131496, 6) (71762, 6) Dask graph 2 chunks in 50 graph layers Data type float64 numpy.ndarray

  • Attributes: (10)

    CREATION_DATE :

    23:26 24-FEB-2021

    Data_Source :

    Global Tropical Moored Buoy Array Project Office/NOAA/PMEL

    File_info :

    Contact: Dai.C.McClurg@noaa.gov

    Request_for_acknowledgement :

    If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115

    _FillValue :

    1.0000000409184788e+35

    array :

    TAO/TRITON

    missing_value :

    1.0000000409184788e+35

    platform_code :

    0n165e

    site_code :

    0n165e

    wmo_platform_code :

    52321

  <xarray.DatasetView>
  Dimensions:                         (time: 131400, zl: 37, zi: 37, nv: 2)
  Coordinates: (12/16)
    * nv                              (nv) float64 1.0 2.0
    * time                            (time) datetime64[ns] 2003-01-01T00:30:00...
      xh                              float64 -140.0
      yh                              float64 0.0625
      yq                              float64 -0.0625
    * zi                              (zi) float64 -523.8 -481.0 ... -2.5 -0.0
      ...                              ...
      oni                             (time) float32 0.92 0.92 ... -0.87 -0.87
      en_mask                         (time) bool True True True ... False False
      ln_mask                         (time) bool False False False ... True True
      warm_mask                       (time) bool False False False ... True True
      cool_mask                       (time) bool True True True ... False False
      enso_transition                 (time) <U12 'El-Nino cool' ... 'La-Nina w...
  Data variables: (12/58)
      KPP_BulkRi                      (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_N2                          (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLT_temp_budget             (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLtransport_heat            (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_OBLdepth                    (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray>
      KPP_buoyFlux                    (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      ...                              ...
      ν                               (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      shear_prod                      (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      eps                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      chi                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      Rif                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      sst                             (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    baseline

  baseline.hb

  • Groups: (0)
  • Dimensions:
    • time: 131400
    • zl: 37
    • zi: 37
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-01T00:30:00 ... 2017-12-...

      array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000',
             '2003-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000',
             '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -523.8 -481.0 -442.5 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22,
             -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57,
             -133.72, -120.66, -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,
              -49.94,  -42.66,  -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,
              -10.  ,   -7.5 ,   -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -547.8 -502.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385,
             -307.605, -283.72 , -261.51 , -240.79 , -221.4  , -203.215, -186.13 ,
             -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6  ,  -91.425,
              -80.98 ,  -71.255,  -62.24 ,  -53.925,  -46.3  ,  -39.355,  -33.075,
              -27.455,  -22.485,  -18.155,  -14.46 ,  -11.385,   -8.75 ,   -6.25 ,
               -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 22 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 24 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(37, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 4.64 MiB 316.52 kiB Shape (37, 131400) (37, 8760) Dask graph 15 chunks in 57 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      0.92 0.92 0.92 ... -0.87 -0.87

      array([ 0.92,  0.92,  0.92, ..., -0.87, -0.87, -0.87], dtype=float32)

    • en_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • warm_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      'El-Nino cool' ... 'La-Nina warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['El-Nino cool', 'El-Nino cool', 'El-Nino cool', ...,
             'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')

  • Data variables: (58)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_NLT_temp_budget
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat content change due to non-local transport, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_kheat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Heat diffusivity due to KPP, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • N2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • S2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of vertical shear used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • T_advection_xy
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Horizontal convergence of residual mean advective fluxes of heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • T_lbdxy_cont_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Lateral diffusion tracer content tendency for T

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Th_tendency_vert_remap
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Vertical remapping tracer content tendency for Heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • boundary_forcing_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Boundary forcing heat tendency

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • frazil_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat tendency due to frazil formation

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • opottempdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_dianeutral_mixing

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • opottemppmdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_mesoscale_neutral_diffusion

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • opottemptend
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of Sea Water Potential Temperature Expressed as Heat Content

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 28 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 51 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 67 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 56 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 63 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 7 graph layers Data type float64 numpy.ndarray

    • ν
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 72 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $ε$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 31 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 123 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    baseline

  <xarray.DatasetView>
  Dimensions:                         (time: 130968, zi: 37, zl: 37, nv: 2)
  Coordinates: (12/16)
    * nv                              (nv) float64 1.0 2.0
    * time                            (time) datetime64[ns] 2003-01-07T00:30:00...
      xh                              float64 -140.0
      yh                              float64 0.0625
      yq                              float64 -0.0625
    * zi                              (zi) float64 -523.8 -481.0 ... -2.5 -0.0
      ...                              ...
      oni                             (time) float32 0.63 0.63 ... -0.87 -0.87
      en_mask                         (time) bool False False ... False False
      ln_mask                         (time) bool False False False ... True True
      warm_mask                       (time) bool True True True ... True True
      cool_mask                       (time) bool False False ... False False
      enso_transition                 (time) <U12 '____________' ... 'La-Nina w...
  Data variables: (12/54)
      KPP_N2                          (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLT_temp_budget             (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLtransport_heat            (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_OBLdepth                    (time) float32 dask.array<chunksize=(130968,), meta=np.ndarray>
      KPP_buoyFlux                    (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_kheat                       (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      ...                              ...
      ν                               (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      shear_prod                      (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      eps                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      chi                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      Rif                             (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      sst                             (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  baseline.kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • time: 130968
    • zi: 37
    • zl: 37
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      cartesian_axis :

      N

      long_name :

      vertex number

      units :

      none

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-07T00:30:00 ... 2017-12-...

      array(['2003-01-07T00:30:00.000000000', '2003-01-07T01:30:00.000000000',
             '2003-01-07T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000',
             '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -523.8 -481.0 -442.5 ... -2.5 -0.0

      cartesian_axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22,
             -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57,
             -133.72, -120.66, -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,
              -49.94,  -42.66,  -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,
              -10.  ,   -7.5 ,   -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -547.8 -502.4 ... -3.75 -1.25

      cartesian_axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385,
             -307.605, -283.72 , -261.51 , -240.79 , -221.4  , -203.215, -186.13 ,
             -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6  ,  -91.425,
              -80.98 ,  -71.255,  -62.24 ,  -53.925,  -46.3  ,  -39.355,  -33.075,
              -27.455,  -22.485,  -18.155,  -14.46 ,  -11.385,   -8.75 ,   -6.25 ,
               -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 22 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 24 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(37, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 4.62 MiB 316.52 kiB Shape (37, 130968) (37, 8760) Dask graph 15 chunks in 57 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      0.63 0.63 0.63 ... -0.87 -0.87

      array([ 0.63,  0.63,  0.63, ..., -0.87, -0.87, -0.87], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... 'La-Nina warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')

  • Data variables: (54)
    • KPP_N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_NLT_temp_budget
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat content change due to non-local transport, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_kheat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Heat diffusivity due to KPP, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • N2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of Brunt-Vaisala frequency used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • S2_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Square of vertical shear used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      1/s2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • T_advection_xy
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Horizontal convergence of residual mean advective fluxes of heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • T_lbdxy_cont_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Lateral diffusion tracer content tendency for T

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      standard_name :

      ocean_vertical_diffusive_heat_flux

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Th_tendency_vert_remap
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Vertical remapping tracer content tendency for Heat

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • boundary_forcing_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Boundary forcing heat tendency

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • frazil_heat_tendency
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Heat tendency due to frazil formation

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • opottempdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_dianeutral_mixing

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • opottemppmdiff
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale neutral diffusion

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content_due_to_parameterized_mesoscale_neutral_diffusion

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • opottemptend
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Tendency of Sea Water Potential Temperature Expressed as Heat Content

      standard_name :

      tendency_of_sea_water_potential_temperature_expressed_as_heat_content

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 28 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 51 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 67 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 56 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(130968,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 511.59 kiB 511.59 kiB Shape (130968,) (130968,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 63 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 36.97 MiB 2.47 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 7 graph layers Data type float64 numpy.ndarray

    • ν
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.49 MiB 1.24 MiB Shape (130968, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 72 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $ε$

      units :

      W/kg

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 31 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.49 MiB 1.20 MiB Shape (130968, 37) (8760, 36) Dask graph 30 chunks in 123 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 511.59 kiB 34.22 kiB Shape (130968,) (8760,) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:               (time: 131400, zl: 37, zi: 37, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 2003-01-01T00:30:00 ... 2017-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 0.92 0.92 0.92 ... -0.87 -0.87 -0.87
      en_mask               (time) bool True True True True ... False False False
      ln_mask               (time) bool False False False False ... True True True
      warm_mask             (time) bool False False False False ... True True True
      cool_mask             (time) bool True True True True ... False False False
      enso_transition       (time) <U12 'El-Nino cool' ... 'La-Nina warm'
  Data variables: (12/49)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      ...                    ...
      ν                     (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KD=0, KV=0

  new_baseline.hb

  • Groups: (0)
  • Dimensions:
    • time: 131400
    • zl: 37
    • zi: 37
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-01T00:30:00 ... 2017-12-...

      array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000',
             '2003-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000',
             '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -523.8 -481.0 -442.5 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22,
             -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57,
             -133.72, -120.66, -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,
              -49.94,  -42.66,  -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,
              -10.  ,   -7.5 ,   -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -547.8 -502.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385,
             -307.605, -283.72 , -261.51 , -240.79 , -221.4  , -203.215, -186.13 ,
             -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6  ,  -91.425,
              -80.98 ,  -71.255,  -62.24 ,  -53.925,  -46.3  ,  -39.355,  -33.075,
              -27.455,  -22.485,  -18.155,  -14.46 ,  -11.385,   -8.75 ,   -6.25 ,
               -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 22 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 24 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(37, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 4.64 MiB 316.52 kiB Shape (37, 131400) (37, 8760) Dask graph 15 chunks in 57 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      0.92 0.92 0.92 ... -0.87 -0.87

      array([ 0.92,  0.92,  0.92, ..., -0.87, -0.87, -0.87], dtype=float32)

    • en_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • warm_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      True True True ... False False

      array([ True,  True,  True, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      'El-Nino cool' ... 'La-Nina warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['El-Nino cool', 'El-Nino cool', 'El-Nino cool', ...,
             'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')

  • Data variables: (49)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 24 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 28 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 51 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 67 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 56 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 10 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 63 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 7 graph layers Data type float64 numpy.ndarray

    • ν
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 72 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $ε$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 31 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 123 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 6 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KD=0, KV=0

  <xarray.DatasetView>
  Dimensions:               (time: 131400, zl: 37, zi: 37, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 2003-01-01T00:30:00 ... 2017-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 0.63 0.63 0.63 ... -0.87 -0.87 -0.87
      en_mask               (time) bool False False False ... False False False
      ln_mask               (time) bool False False False False ... True True True
      warm_mask             (time) bool True True True True ... True True True
      cool_mask             (time) bool False False False ... False False False
      enso_transition       (time) <U12 '____________' ... 'La-Nina warm'
  Data variables: (12/49)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      ...                    ...
      ν                     (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  new_baseline.kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • time: 131400
    • zl: 37
    • zi: 37
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-01T00:30:00 ... 2017-12-...

      array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000',
             '2003-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000',
             '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -523.8 -481.0 -442.5 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22,
             -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57,
             -133.72, -120.66, -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,
              -49.94,  -42.66,  -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,
              -10.  ,   -7.5 ,   -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -547.8 -502.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385,
             -307.605, -283.72 , -261.51 , -240.79 , -221.4  , -203.215, -186.13 ,
             -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6  ,  -91.425,
              -80.98 ,  -71.255,  -62.24 ,  -53.925,  -46.3  ,  -39.355,  -33.075,
              -27.455,  -22.485,  -18.155,  -14.46 ,  -11.385,   -8.75 ,   -6.25 ,
               -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(37, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 4.64 MiB 316.52 kiB Shape (37, 131400) (37, 8760) Dask graph 15 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      0.63 0.63 0.63 ... -0.87 -0.87

      array([ 0.63,  0.63,  0.63, ..., -0.87, -0.87, -0.87], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... 'La-Nina warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')

  • Data variables: (49)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float64 numpy.ndarray

    • ν
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $ε$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:               (time: 131400, zl: 37, zi: 37, nv: 2)
  Coordinates: (12/16)
    * nv                    (nv) float64 1.0 2.0
    * time                  (time) datetime64[ns] 2003-01-01T00:30:00 ... 2017-...
      xh                    float64 -140.0
      yh                    float64 0.0625
      yq                    float64 -0.0625
    * zi                    (zi) float64 -523.8 -481.0 -442.5 ... -5.0 -2.5 -0.0
      ...                    ...
      oni                   (time) float32 0.63 0.63 0.63 ... -0.87 -0.87 -0.87
      en_mask               (time) bool False False False ... False False False
      ln_mask               (time) bool False False False False ... True True True
      warm_mask             (time) bool True True True True ... True True True
      cool_mask             (time) bool False False False ... False False False
      enso_transition       (time) <U12 '____________' ... 'La-Nina warm'
  Data variables: (12/49)
      KPP_BulkRi            (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_NLtransport_heat  (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_OBLdepth          (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray>
      KPP_buoyFlux          (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      KPP_ustar             (time) float32 dask.array<chunksize=(131400,), meta=np.ndarray>
      KS_extra              (time, zi) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      ...                    ...
      ν                     (time, zl) float32 dask.array<chunksize=(8760, 37), meta=np.ndarray>
      shear_prod            (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      eps                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      chi                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      Rif                   (time, zi) float32 dask.array<chunksize=(8760, 36), meta=np.ndarray>
      sst                   (time) float32 dask.array<chunksize=(8760,), meta=np.ndarray>
  Attributes:
      title:    KPP ν0=2.5, Ri0=0.5

  new_baseline.kpp.lmd.005

  • Groups: (0)
  • Dimensions:
    • time: 131400
    • zl: 37
    • zi: 37
    • nv: 2
  • Coordinates: (16)
    • nv
      (nv)
      float64
      1.0 2.0

      long_name :

      vertex number

      array([1., 2.])

    • time
      (time)
      datetime64[ns]
      2003-01-01T00:30:00 ... 2017-12-...

      array(['2003-01-01T00:30:00.000000000', '2003-01-01T01:30:00.000000000',
             '2003-01-01T02:30:00.000000000', ..., '2017-12-31T21:30:00.000000000',
             '2017-12-31T22:30:00.000000000', '2017-12-31T23:30:00.000000000'],
            dtype='datetime64[ns]')

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zi
      (zi)
      float64
      -523.8 -481.0 -442.5 ... -2.5 -0.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-523.8 , -481.01, -442.51, -407.64, -375.88, -346.78, -319.99, -295.22,
             -272.22, -250.8 , -230.78, -212.02, -194.41, -177.85, -162.26, -147.57,
             -133.72, -120.66, -108.37,  -96.83,  -86.02,  -75.94,  -66.57,  -57.91,
              -49.94,  -42.66,  -36.05,  -30.1 ,  -24.81,  -20.16,  -16.15,  -12.77,
              -10.  ,   -7.5 ,   -5.  ,   -2.5 ,   -0.  ])

    • zl
      (zl)
      float64
      -547.8 -502.4 ... -3.75 -1.25

      axis :

      Z

      long_name :

      Layer pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-547.75 , -502.405, -461.76 , -425.075, -391.76 , -361.33 , -333.385,
             -307.605, -283.72 , -261.51 , -240.79 , -221.4  , -203.215, -186.13 ,
             -170.055, -154.915, -140.645, -127.19 , -114.515, -102.6  ,  -91.425,
              -80.98 ,  -71.255,  -62.24 ,  -53.925,  -46.3  ,  -39.355,  -33.075,
              -27.455,  -22.485,  -18.155,  -14.46 ,  -11.385,   -8.75 ,   -6.25 ,
               -3.75 ,   -1.25 ])

    • eucmax
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      units :

      m

      long_name :

      EUC maximum

      positive :

      up

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 21 graph layers Data type float64 numpy.ndarray

    • mldT
      (time)
      float64
      dask.array<chunksize=(8760,), meta=np.ndarray>

      long_name :

      MLD$_θ$

      units :

      m

      description :

      Interpolate θi to 1m grid. Search for max depth where |dθ| > 0.15

      Array Chunk Bytes 1.00 MiB 68.44 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 23 graph layers Data type float64 numpy.ndarray

    • dcl_mask
      (zi, time)
      bool
      dask.array<chunksize=(37, 8760), meta=np.ndarray>

      description :

      True when 5m below mldT and above eucmax.

      Array Chunk Bytes 4.64 MiB 316.52 kiB Shape (37, 131400) (37, 8760) Dask graph 15 chunks in 56 graph layers Data type bool numpy.ndarray

    • oni
      (time)
      float32
      0.63 0.63 0.63 ... -0.87 -0.87

      array([ 0.63,  0.63,  0.63, ..., -0.87, -0.87, -0.87], dtype=float32)

    • en_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • ln_mask
      (time)
      bool
      False False False ... True True

      array([False, False, False, ...,  True,  True,  True])

    • warm_mask
      (time)
      bool
      True True True ... True True True

      array([ True,  True,  True, ...,  True,  True,  True])

    • cool_mask
      (time)
      bool
      False False False ... False False

      array([False, False, False, ..., False, False, False])

    • enso_transition
      (time)
      <U12
      '____________' ... 'La-Nina warm'

      description :

      Warner & Moum (2019) ENSO transition phase; El-Nino = ONI > 0.5 for at least 6 months; La-Nina = ONI < -0.5 for at least 6 months

      array(['____________', '____________', '____________', ...,
             'La-Nina warm', 'La-Nina warm', 'La-Nina warm'], dtype='<U12')

  • Data variables: (49)
    • KPP_BulkRi
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Bulk Richardson number used to find the OBL depth used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_NLtransport_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Non-local transport (Cs*G(sigma)) for heat, as calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_OBLdepth
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Thickness of the surface Ocean Boundary Layer calculated by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      meter

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KPP_buoyFlux
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Surface (and penetrating) buoyancy flux, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2/s3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KPP_ustar
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Friction velocity, u*, as used by [CVMix] KPP

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m/s

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • KS_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • KT_extra
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Double-diffusive diffusivity for temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_heat
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kd_salt
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for salt at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_u
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_x_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Kv_v
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at v-points

      standard_name :

      ocean_vertical_y_viscosity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • N2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • SSH
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea Surface Height

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Shortwave radiation flux into ocean

      standard_name :

      net_downward_shortwave_flux_at_sea_water_surface

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • SW_pen
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Penetrating shortwave radiation flux into ocean

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • Tflx_dia_diff
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • dens
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m^3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • densT
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_density

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      kg/m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • h
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:sum yh:mean xh:mean time: mean

      long_name :

      Layer Thickness

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • mlotst
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Ocean Mixed Layer Thickness Defined by Sigma T

      standard_name :

      ocean_mixed_layer_thickness_defined_by_sigma_t

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • net_heat_surface
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Surface ocean heat flux from SW+LW+lat+sens+mass transfer+frazil+restore+seaice_melt_heat or flux adjustments

      standard_name :

      surface_downward_heat_flux_in_sea_water

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      W m-2

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Gradient Richarson number used by MOM_CVMix_shear module

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • ri_grad_shear_orig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Original gradient Richarson number, before smoothing was applied. This is part of the MOM_CVMix_shear module and only available

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      nondim

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • so
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Salinity

      standard_name :

      sea_water_salinity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      psu

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • taux
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      standard_name :

      surface_downward_x_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • tauy
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Meridional surface stress ocean interactions with atmos and ice

      standard_name :

      surface_downward_y_stress

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • thetao
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • uo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • vo
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • volcello
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      area:sum zl:sum yh:sum xh:sum time: mean

      long_name :

      Ocean grid-cell volume

      standard_name :

      ocean_volume

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m3

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • zos
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean yh:mean xh:mean time: mean

      long_name :

      Sea surface height above geoid

      standard_name :

      sea_surface_height_above_geoid

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

    • α
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_thermal_expansion_coefficient

      units :

      C-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • β
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      standard_name :

      sea_water_haline_contraction_coefficient

      units :

      kg/g

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • Tz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $T_z$

      units :

      Cm$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray

    • Sz
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S_z$

      units :

      m$^{-1}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 23 graph layers Data type float32 numpy.ndarray

    • N2T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $N_T^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 27 graph layers Data type float32 numpy.ndarray

    • S2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $S^2$

      units :

      s$^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 50 graph layers Data type float32 numpy.ndarray

    • shred2
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Sh_{red}^2$

      units :

      $s^{-2}$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 66 graph layers Data type float32 numpy.ndarray

    • Rig_T
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $Ri^g_T$

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 65 graph layers Data type float32 numpy.ndarray

    • Rig
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 55 graph layers Data type float32 numpy.ndarray

    • tau
      (time)
      float32
      dask.array<chunksize=(131400,), meta=np.ndarray>

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      Array Chunk Bytes 513.28 kiB 513.28 kiB Shape (131400,) (131400,) Dask graph 1 chunks in 9 graph layers Data type float32 numpy.ndarray

    • Jb
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 62 graph layers Data type float32 numpy.ndarray

    • Jq
      (time, zi)
      float64
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      units :

      W/m^2

      long_name :

      $J_q^t$

      Array Chunk Bytes 37.09 MiB 2.47 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 6 graph layers Data type float64 numpy.ndarray

    • ν
      (time, zl)
      float32
      dask.array<chunksize=(8760, 37), meta=np.ndarray>

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.24 MiB Shape (131400, 37) (8760, 37) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

    • shear_prod
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $SP$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 71 graph layers Data type float32 numpy.ndarray

    • eps
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $ε$

      units :

      W/kg

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 121 graph layers Data type float32 numpy.ndarray

    • chi
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      long_name :

      $χ$

      units :

      C^2/s

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 30 graph layers Data type float32 numpy.ndarray

    • Rif
      (time, zi)
      float32
      dask.array<chunksize=(8760, 36), meta=np.ndarray>

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      flux_richardson_number

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      Array Chunk Bytes 18.55 MiB 1.20 MiB Shape (131400, 37) (8760, 36) Dask graph 30 chunks in 122 graph layers Data type float32 numpy.ndarray

    • sst
      (time)
      float32
      dask.array<chunksize=(8760,), meta=np.ndarray>

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      $SST$

      standard_name :

      sea_surface_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      Array Chunk Bytes 513.28 kiB 34.22 kiB Shape (131400,) (8760,) Dask graph 15 chunks in 5 graph layers Data type float32 numpy.ndarray

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ri0=0.5

• Dimensions:
• Coordinates: (0)
• Data variables: (0)
• Attributes: (0)

N2-S2 histograms

ref = tree["TAO"].ds.reset_coords(drop=True).cf.sel(Z=slice(-120, None))
counts = np.minimum(ref["S2"].cf.count("Z"), ref["N2T"].cf.count("Z")).load()


def calc_histograms(ds):
    ds = ds.copy()
    ds["tao_mask"] = counts.reindex(time=ds.time, method="nearest") > 5
    ds["tao_mask"].attrs = {
        "description": "True when there are more than 5 5-m T, u, v in TAO dataset"
    }
    # I did try masking out the model like the data
    # ds = ds.where(ds.tao_mask)
    return ds.update(mixpods.pdf_N2S2(ds))


tree = tree.map_over_subtree(calc_histograms)

daily composite

dailies = tree.map_over_subtree(mixpods.daily_composites)
dailies

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1217: RuntimeWarning: All-NaN slice encountered
  r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out,

<xarray.DatasetView>
Dimensions:  ()
Data variables:
    *empty*

datatree.DataTree

• Groups: (6)
  <xarray.DatasetView>
  Dimensions:             (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth               (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      latitude            float32 0.0
      longitude           float32 -140.0
      reference_pressure  int64 0
    * hour                (hour) int64 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22 23
    * tau_bins            (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT                  (depth, hour, tau_bins) float64 9.959e-06 ... 0.0001646
      eps                 (depth, hour, tau_bins) float64 4.205e-09 ... 1.608e-08
      chi                 (depth, hour, tau_bins) float64 1.67e-08 ... 1.95e-08
      Jb                  (depth, hour, tau_bins) float64 nan nan nan ... nan nan
      Jq                  (depth, hour, tau_bins) float64 -1.003 -1.842 ... -4.428
      S2                  (depth, hour, tau_bins) float32 0.0001345 ... 0.0001682
      N2                  (depth, hour, tau_bins) float64 0.0002082 ... 2.895e-05
      Rig                 (depth, hour, tau_bins) float64 1.406 1.252 ... 0.05421
      Rig_T               (depth, hour, tau_bins) float64 1.901 1.204 ... 0.1185
      tau                 (hour, tau_bins) float64 0.02709 0.05733 ... 0.09308
  Attributes:
      CREATION_DATE:                23:26 24-FEB-2021
      Data_Source:                  Global Tropical Moored Buoy Array Project O...
      File_info:                    Contact: Dai.C.McClurg@noaa.gov
      Request_for_acknowledgement:  If you use these data in publications or pr...
      _FillValue:                   1.0000000409184788e+35
      array:                        TAO/TRITON
      missing_value:                1.0000000409184788e+35
      platform_code:                0n165e
      site_code:                    0n165e
      wmo_platform_code:            52321

  TAO

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      axis :

      Z

      positive :

      up

      units :

      m

      array([-89., -69., -59., -49., -39., -29.])

    • latitude
      ()
      float32
      0.0

      array(0., dtype=float32)

    • longitude
      ()
      float32
      -140.0

      array(-140., dtype=float32)

    • reference_pressure
      ()
      int64
      0

      array(0)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float64
      9.959e-06 1.107e-05 ... 0.0001646

      standard_name :

      ocean_vertical_heat_diffusivity

      array([[[9.95869314e-06, 1.10713814e-05, 1.81385305e-05],
              [7.62679606e-06, 9.84649659e-06, 1.98903482e-05],
              [8.26769194e-06, 1.03487366e-05, 1.69229065e-05],
              [5.09341517e-06, 1.06415264e-05, 9.94711354e-06],
              [6.27289392e-06, 1.01286448e-05, 9.09556433e-06],
              [5.85044689e-06, 9.09517149e-06, 1.06714061e-05],
              [5.92212630e-06, 8.40077865e-06, 1.11052356e-05],
              [6.47377658e-06, 9.78053302e-06, 1.13559910e-05],
              [5.77007820e-06, 1.00437481e-05, 1.54385405e-05],
              [5.60030739e-06, 8.32575368e-06, 1.31285365e-05],
              [5.27227053e-06, 7.15577324e-06, 1.44267402e-05],
              [5.03200713e-06, 9.93609875e-06, 1.68541630e-05],
              [6.42531180e-06, 1.04701955e-05, 1.45938509e-05],
              [5.45290240e-06, 1.06380336e-05, 1.94790247e-05],
              [5.05695470e-06, 1.08870254e-05, 2.13555496e-05],
              [6.78671892e-06, 1.23528905e-05, 2.01087241e-05],
              [9.80084606e-06, 1.38124457e-05, 2.85315836e-05],
              [6.90458131e-06, 1.42042099e-05, 2.59700678e-05],
              [8.44895131e-06, 1.38211283e-05, 2.92244979e-05],
              [6.96328801e-06, 1.80749482e-05, 2.81233184e-05],
      ...
              [2.49796366e-05, 1.51222154e-04, 1.54133302e-03],
              [2.54375198e-05, 1.92556637e-04, 1.98323078e-03],
              [2.95016029e-05, 2.74405783e-04, 2.23706350e-03],
              [2.91381387e-05, 5.29042782e-04, 2.46231496e-03],
              [3.22679193e-05, 6.25229486e-04, 2.36063586e-03],
              [4.87863828e-05, 6.82247064e-04, 2.44611497e-03],
              [7.65505437e-05, 8.72437582e-04, 2.08043411e-03],
              [9.60910794e-05, 7.32102430e-04, 2.33006858e-03],
              [8.45055247e-05, 8.06508643e-04, 1.81524742e-03],
              [9.68881747e-05, 7.19295971e-04, 2.07390795e-03],
              [1.12205990e-04, 8.31837440e-04, 1.82439093e-03],
              [1.25092879e-04, 8.00568096e-04, 1.85697065e-03],
              [1.41021393e-04, 7.84944210e-04, 1.71427588e-03],
              [1.35184262e-04, 7.04609354e-04, 1.41404584e-03],
              [1.12211968e-04, 3.97215676e-04, 1.07973119e-03],
              [8.04180511e-05, 2.89701497e-04, 6.65654642e-04],
              [6.64714222e-05, 1.94172831e-04, 4.08622947e-04],
              [5.75134301e-05, 1.40309406e-04, 2.67942773e-04],
              [5.13853203e-05, 1.01548040e-04, 1.64550217e-04],
              [4.10933487e-05, 8.66966898e-05, 1.64556856e-04]]])

    • eps
      (depth, hour, tau_bins)
      float64
      4.205e-09 6.427e-09 ... 1.608e-08

      array([[[4.20484047e-09, 6.42665336e-09, 1.03495228e-08],
              [3.39942155e-09, 6.09951608e-09, 9.95406422e-09],
              [3.22205779e-09, 6.00608012e-09, 9.89785322e-09],
              [2.43384740e-09, 5.65188162e-09, 6.43540665e-09],
              [2.34056719e-09, 4.68248253e-09, 5.86884639e-09],
              [2.42516755e-09, 4.36594516e-09, 6.04769734e-09],
              [2.01203910e-09, 4.50894201e-09, 6.80387559e-09],
              [2.77474401e-09, 4.06819005e-09, 5.78686391e-09],
              [3.02695691e-09, 4.19429197e-09, 9.25259960e-09],
              [2.16803642e-09, 4.07562895e-09, 7.99217582e-09],
              [2.25024698e-09, 3.86947366e-09, 1.08315576e-08],
              [2.34519609e-09, 4.58983072e-09, 8.80408647e-09],
              [2.43251603e-09, 5.04441870e-09, 1.00646850e-08],
              [2.87316856e-09, 4.50655083e-09, 1.06291960e-08],
              [3.21955035e-09, 5.40452670e-09, 1.13614463e-08],
              [2.93556264e-09, 5.97819732e-09, 1.35444442e-08],
              [3.32274328e-09, 6.36330330e-09, 1.69035479e-08],
              [3.41472292e-09, 7.45018817e-09, 1.40619889e-08],
              [4.16563716e-09, 6.50261284e-09, 1.49772749e-08],
              [3.93711329e-09, 8.03877507e-09, 1.71921395e-08],
      ...
              [4.32062308e-09, 2.14336097e-08, 1.84017958e-07],
              [4.32244969e-09, 3.23795526e-08, 1.77984766e-07],
              [4.99443371e-09, 4.76849714e-08, 1.82287235e-07],
              [6.67475746e-09, 7.43192793e-08, 1.80947783e-07],
              [7.69190689e-09, 8.48603912e-08, 1.83042578e-07],
              [1.24068720e-08, 8.70767941e-08, 1.63145058e-07],
              [1.75400970e-08, 9.81732289e-08, 1.55381279e-07],
              [2.07108437e-08, 8.34432489e-08, 1.47163501e-07],
              [2.09570176e-08, 9.55806383e-08, 1.28104332e-07],
              [2.49357959e-08, 7.48081255e-08, 1.38691872e-07],
              [2.02488017e-08, 7.54215709e-08, 1.17639124e-07],
              [2.72920187e-08, 7.42685031e-08, 1.22579504e-07],
              [2.84401490e-08, 7.54687544e-08, 1.20862216e-07],
              [2.32893736e-08, 6.57975258e-08, 9.00575173e-08],
              [1.80033299e-08, 4.22211119e-08, 6.23893426e-08],
              [1.57912073e-08, 2.80175873e-08, 4.02353834e-08],
              [1.23950854e-08, 2.20880606e-08, 2.95446015e-08],
              [1.11499973e-08, 1.59336048e-08, 1.98472383e-08],
              [8.36356549e-09, 1.27149248e-08, 1.63302630e-08],
              [7.16264221e-09, 1.10760452e-08, 1.60790152e-08]]])

    • chi
      (depth, hour, tau_bins)
      float64
      1.67e-08 3.739e-08 ... 1.95e-08

      array([[[1.67021892e-08, 3.73895360e-08, 7.93282652e-08],
              [1.45695729e-08, 3.21540306e-08, 7.51363908e-08],
              [1.66509708e-08, 3.17736964e-08, 7.22932589e-08],
              [1.00107661e-08, 3.13534986e-08, 4.77883670e-08],
              [1.33179056e-08, 2.40084728e-08, 3.57552816e-08],
              [9.22712244e-09, 2.50863744e-08, 4.33211842e-08],
              [8.08744573e-09, 2.38605289e-08, 3.82013297e-08],
              [1.09612925e-08, 2.27882209e-08, 3.99283958e-08],
              [1.34920747e-08, 1.97982703e-08, 4.72313840e-08],
              [9.85650711e-09, 2.04048889e-08, 5.14985775e-08],
              [1.08937936e-08, 2.35725432e-08, 6.87134261e-08],
              [1.21212706e-08, 2.51579652e-08, 7.15330504e-08],
              [1.32546719e-08, 3.39587720e-08, 9.47722781e-08],
              [1.69953873e-08, 2.60973815e-08, 7.29116184e-08],
              [1.67531863e-08, 3.07021553e-08, 6.79356643e-08],
              [1.77120188e-08, 4.09737308e-08, 7.29900722e-08],
              [1.93716942e-08, 4.31379369e-08, 9.26064400e-08],
              [2.09236218e-08, 4.34670007e-08, 9.19779817e-08],
              [2.48015149e-08, 3.95476993e-08, 8.28554984e-08],
              [2.05587984e-08, 5.27063058e-08, 8.39105570e-08],
      ...
              [6.58562963e-09, 4.00100193e-08, 1.82930124e-07],
              [8.27051334e-09, 5.94085776e-08, 1.62815748e-07],
              [1.05461194e-08, 7.08934926e-08, 1.68249950e-07],
              [1.42336592e-08, 9.11751999e-08, 1.81170217e-07],
              [1.72444375e-08, 9.91038146e-08, 1.50109762e-07],
              [2.48941347e-08, 1.02137827e-07, 1.58365512e-07],
              [3.49353545e-08, 1.15828778e-07, 1.27671006e-07],
              [4.11614687e-08, 9.85426121e-08, 1.36028494e-07],
              [3.54397051e-08, 9.90287508e-08, 1.15470101e-07],
              [4.15427310e-08, 8.94060867e-08, 1.12016269e-07],
              [3.92166332e-08, 8.64900353e-08, 9.95322983e-08],
              [5.45326891e-08, 8.52935771e-08, 1.10918666e-07],
              [4.42275054e-08, 7.57406371e-08, 9.45690680e-08],
              [4.10326472e-08, 7.06337841e-08, 7.52282802e-08],
              [3.53795830e-08, 4.58133432e-08, 5.44219219e-08],
              [2.37814091e-08, 3.15729107e-08, 3.78253266e-08],
              [1.95929775e-08, 2.64875960e-08, 3.30521637e-08],
              [1.66729435e-08, 2.15147840e-08, 2.22266528e-08],
              [1.50428825e-08, 1.53505017e-08, 1.73559861e-08],
              [1.08272541e-08, 1.40830926e-08, 1.94961160e-08]]])

    • Jb
      (depth, hour, tau_bins)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      array([[[nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
      ...
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan],
              [nan, nan, nan]]])

    • Jq
      (depth, hour, tau_bins)
      float64
      -1.003 -1.842 ... -2.971 -4.428

      array([[[ -1.00256446,  -1.84244745,  -2.80850936],
              [ -0.91887084,  -1.62464068,  -2.56282757],
              [ -0.88374135,  -1.58679603,  -2.88823456],
              [ -0.70443327,  -1.6103817 ,  -1.77298658],
              [ -0.68665724,  -1.27295597,  -1.77228   ],
              [ -0.66664796,  -1.19898684,  -1.67626741],
              [ -0.59730391,  -1.23465212,  -1.70404798],
              [ -0.7235757 ,  -1.18544042,  -1.59040206],
              [ -0.75019594,  -1.04231212,  -2.57469427],
              [ -0.57483421,  -1.14463015,  -2.24184293],
              [ -0.63752255,  -1.10632234,  -2.90389878],
              [ -0.65130446,  -1.26093457,  -2.35183439],
              [ -0.66722366,  -1.3861301 ,  -2.72893158],
              [ -0.81851688,  -1.24810229,  -2.85460253],
              [ -0.72343038,  -1.27913762,  -2.93287239],
              [ -0.82057933,  -1.73613097,  -3.35338815],
              [ -0.92375014,  -1.80837082,  -4.44104197],
              [ -0.87323403,  -2.01851581,  -3.77664889],
              [ -1.19708228,  -1.7267686 ,  -4.23530912],
              [ -0.98020427,  -2.19262619,  -4.13656709],
      ...
              [ -1.11297409,  -5.84520527, -47.88722398],
              [ -1.20329183,  -9.40240756, -45.37124552],
              [ -1.33200184, -12.95762863, -46.64407742],
              [ -1.73752661, -19.31055455, -45.85240017],
              [ -2.00999545, -20.92691833, -45.36418532],
              [ -3.38542206, -21.6217159 , -41.41348273],
              [ -4.660082  , -25.72382479, -37.29098972],
              [ -5.53276281, -20.1004968 , -36.00067158],
              [ -5.56787429, -22.40393839, -31.23276616],
              [ -5.82381354, -19.36849336, -34.99038496],
              [ -5.01723603, -19.13635109, -28.69033267],
              [ -6.73045836, -19.73596063, -28.18919034],
              [ -7.09852648, -18.76319064, -30.2266072 ],
              [ -6.12788   , -16.48818028, -21.37988398],
              [ -4.62785644, -10.76205284, -16.19281641],
              [ -4.21420556,  -7.65540787, -10.55800383],
              [ -3.29425731,  -5.96463463,  -7.87434395],
              [ -2.86694385,  -4.28650835,  -5.42953264],
              [ -2.33320233,  -3.38921404,  -4.47756404],
              [ -1.82422195,  -2.97098078,  -4.42772743]]])

    • S2
      (depth, hour, tau_bins)
      float32
      0.0001345 0.0002001 ... 0.0001682

      long_name :

      $S^2$

      array([[[0.00013448, 0.00020006, 0.00030025],
              [0.00013459, 0.00019925, 0.00030133],
              [0.00013651, 0.00019142, 0.0003029 ],
              [0.00012919, 0.00020452, 0.00029948],
              [0.00013896, 0.0002124 , 0.00030397],
              [0.00015213, 0.00019731, 0.00031407],
              [0.00014794, 0.00019219, 0.00030202],
              [0.00015217, 0.00019176, 0.00030711],
              [0.0001403 , 0.00019476, 0.00029934],
              [0.00013978, 0.00018397, 0.00030806],
              [0.00014281, 0.00018728, 0.00030986],
              [0.00014091, 0.00018794, 0.00031204],
              [0.00013823, 0.0001874 , 0.000305  ],
              [0.00013669, 0.00019039, 0.00031266],
              [0.00013547, 0.00019014, 0.00030768],
              [0.0001324 , 0.00018962, 0.00029873],
              [0.00013033, 0.00018335, 0.00029489],
              [0.00013093, 0.0001916 , 0.00030148],
              [0.00014072, 0.00018797, 0.00031962],
              [0.00014378, 0.00018233, 0.00031395],
      ...
              [0.00030681, 0.00022737, 0.00020181],
              [0.00031573, 0.00023622, 0.000205  ],
              [0.00032313, 0.0002395 , 0.00019791],
              [0.00032533, 0.00024731, 0.00017745],
              [0.0003447 , 0.00024576, 0.00016273],
              [0.00036876, 0.0002621 , 0.00016414],
              [0.0003664 , 0.000238  , 0.00016017],
              [0.00036569, 0.00023603, 0.00015653],
              [0.00035129, 0.00023055, 0.00014936],
              [0.00035962, 0.00021455, 0.00015314],
              [0.00033461, 0.00022228, 0.00015203],
              [0.00038251, 0.00024297, 0.00017307],
              [0.00040409, 0.000252  , 0.0001731 ],
              [0.00037549, 0.00023915, 0.00015291],
              [0.00036383, 0.00021962, 0.0001535 ],
              [0.00036171, 0.00020565, 0.0001415 ],
              [0.0003339 , 0.00020438, 0.00015409],
              [0.00035724, 0.00019812, 0.00015214],
              [0.0003461 , 0.00020423, 0.00015108],
              [0.0003532 , 0.00020651, 0.00016817]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float64
      0.0002082 0.0002229 ... 2.895e-05

      long_name :

      $N^2$

      array([[[2.08151738e-04, 2.22862104e-04, 2.14441416e-04],
              [2.16603991e-04, 2.24483495e-04, 2.21728203e-04],
              [2.15355923e-04, 2.21838325e-04, 2.18587475e-04],
              [2.15544742e-04, 2.22885541e-04, 2.12326524e-04],
              [2.16644011e-04, 2.13538050e-04, 2.23611350e-04],
              [2.09853593e-04, 2.20879385e-04, 2.19642997e-04],
              [1.98066216e-04, 2.21105441e-04, 2.18486263e-04],
              [2.16155790e-04, 2.18854377e-04, 2.23897831e-04],
              [2.21096656e-04, 2.18472571e-04, 2.21506161e-04],
              [2.16910676e-04, 2.20828730e-04, 2.15561007e-04],
              [2.20007830e-04, 2.25418650e-04, 2.14505446e-04],
              [2.13170636e-04, 2.18209400e-04, 2.26985607e-04],
              [2.08379942e-04, 2.20359529e-04, 2.28006372e-04],
              [2.18869956e-04, 2.12548021e-04, 2.37007497e-04],
              [2.22657337e-04, 2.18700017e-04, 2.19859678e-04],
              [2.16105074e-04, 2.18497812e-04, 2.29437082e-04],
              [2.15577658e-04, 2.11075774e-04, 2.20386303e-04],
              [2.14909841e-04, 2.19245199e-04, 2.10944560e-04],
              [2.18744613e-04, 2.23280408e-04, 2.02637232e-04],
              [2.18981413e-04, 2.22255352e-04, 2.07395227e-04],
      ...
              [6.96024719e-05, 4.77216368e-05, 3.52536021e-05],
              [7.21013450e-05, 4.76598289e-05, 3.29697584e-05],
              [7.22574894e-05, 5.01282745e-05, 3.12980790e-05],
              [7.68367868e-05, 4.81516730e-05, 3.00344601e-05],
              [7.68827915e-05, 4.78022934e-05, 2.65751683e-05],
              [7.66054714e-05, 4.80892891e-05, 2.51018158e-05],
              [7.63678736e-05, 4.39940175e-05, 2.65891577e-05],
              [7.64221137e-05, 4.27375694e-05, 2.65801164e-05],
              [7.63855405e-05, 4.13222946e-05, 2.54512380e-05],
              [7.23437008e-05, 3.95318950e-05, 2.40070337e-05],
              [7.32406023e-05, 3.87474723e-05, 2.17285776e-05],
              [7.84975393e-05, 3.86002397e-05, 2.22512416e-05],
              [7.96900786e-05, 3.91479868e-05, 2.12247197e-05],
              [7.60129546e-05, 3.42026320e-05, 2.17803309e-05],
              [7.44090646e-05, 3.69594533e-05, 2.22136917e-05],
              [7.49782129e-05, 3.66955626e-05, 2.24673463e-05],
              [7.37285223e-05, 3.66268637e-05, 2.49871805e-05],
              [7.33997174e-05, 3.74016674e-05, 2.55136179e-05],
              [7.08907159e-05, 4.04018172e-05, 2.61858948e-05],
              [6.88821220e-05, 4.19587334e-05, 2.89518715e-05]]])

    • Rig
      (depth, hour, tau_bins)
      float64
      1.406 1.252 ... 0.07694 0.05421

      long_name :

      $Ri^g$

      array([[[1.40644738, 1.25178115, 0.74488556],
              [1.41762533, 1.35060308, 0.76782803],
              [1.47274045, 1.28796047, 0.71477707],
              [1.77206894, 1.23581944, 0.85990554],
              [1.42856916, 1.18887077, 0.86747588],
              [1.37627981, 1.250239  , 0.79682319],
              [1.35225077, 1.14314482, 0.89240256],
              [1.53488295, 1.13362321, 0.81762861],
              [1.43149522, 1.18854333, 0.82108334],
              [1.69124541, 1.31780546, 0.74364754],
              [1.63997589, 1.2650186 , 0.75719731],
              [1.60196071, 1.29321876, 0.80676174],
              [1.76211032, 1.29848346, 0.82638659],
              [1.89364181, 1.2401134 , 0.81697784],
              [1.75861073, 1.2281106 , 0.82555718],
              [1.79334254, 1.25421119, 0.87983679],
              [1.75460255, 1.23742487, 0.81284393],
              [1.6443649 , 1.20305246, 0.80500277],
              [1.90235508, 1.39715558, 0.75862198],
              [1.59556172, 1.27929263, 0.69249317],
      ...
              [0.11922037, 0.08834196, 0.05390135],
              [0.11364714, 0.0985151 , 0.04969669],
              [0.11955433, 0.09013164, 0.04259646],
              [0.13152116, 0.08148522, 0.04038412],
              [0.12462708, 0.07752266, 0.0412092 ],
              [0.1059685 , 0.08015321, 0.03483511],
              [0.11336615, 0.07559725, 0.03430897],
              [0.10954652, 0.07044978, 0.03097055],
              [0.10013768, 0.07146289, 0.03221148],
              [0.08570449, 0.07030557, 0.0349513 ],
              [0.09017043, 0.06546468, 0.03168679],
              [0.08669634, 0.0582093 , 0.03019886],
              [0.09715547, 0.06009826, 0.02640776],
              [0.07935822, 0.05776852, 0.03262731],
              [0.09296572, 0.05099493, 0.03820643],
              [0.08410832, 0.06245423, 0.04184208],
              [0.10067066, 0.0679023 , 0.04833413],
              [0.10772209, 0.06960988, 0.05154636],
              [0.096249  , 0.07780057, 0.05655469],
              [0.10424006, 0.07694161, 0.05421351]]])

    • Rig_T
      (depth, hour, tau_bins)
      float64
      1.901 1.204 0.6325 ... 0.12 0.1185

      long_name :

      $Ri^g_T$

      array([[[1.90144926, 1.20402004, 0.63250394],
              [1.97722436, 1.16803555, 0.63929729],
              [1.82363029, 1.19271362, 0.63758984],
              [2.00858352, 1.17413288, 0.64556353],
              [1.764249  , 1.08857607, 0.63981687],
              [1.6722678 , 1.14029434, 0.63841363],
              [1.64416596, 1.07404686, 0.69016643],
              [1.80985361, 1.1286188 , 0.66627749],
              [1.84750455, 1.11619713, 0.65720696],
              [1.85832509, 1.14625708, 0.64646297],
              [1.82383741, 1.28825209, 0.60547409],
              [1.90534517, 1.22815954, 0.65631865],
              [1.92757436, 1.17589125, 0.6729475 ],
              [1.95646798, 1.15944217, 0.67811545],
              [2.15228629, 1.2346199 , 0.68925813],
              [2.17114092, 1.25440367, 0.67767255],
              [1.9283005 , 1.25106341, 0.65996391],
              [2.09583235, 1.16882993, 0.64695664],
              [2.00001933, 1.18052973, 0.63612982],
              [1.95096458, 1.24909609, 0.60900943],
      ...
              [0.14035465, 0.15645461, 0.13741426],
              [0.15093877, 0.1540195 , 0.12269896],
              [0.1517013 , 0.14701272, 0.10974087],
              [0.15332836, 0.13742055, 0.10176559],
              [0.14785674, 0.1249745 , 0.09094855],
              [0.14468148, 0.11747535, 0.08905629],
              [0.1371656 , 0.11502819, 0.08797114],
              [0.13831776, 0.10755145, 0.08060554],
              [0.1255792 , 0.1066724 , 0.07354782],
              [0.11734482, 0.09774099, 0.07285993],
              [0.12316585, 0.0914999 , 0.07129744],
              [0.11004655, 0.0806289 , 0.05700402],
              [0.12113939, 0.07903602, 0.04899825],
              [0.11156332, 0.08470378, 0.06233322],
              [0.11291802, 0.09061568, 0.07497081],
              [0.12169541, 0.09945144, 0.08995757],
              [0.12816946, 0.11179433, 0.0962672 ],
              [0.13384635, 0.11637368, 0.11296923],
              [0.12542545, 0.12154479, 0.11954271],
              [0.12356876, 0.12001706, 0.11848762]]])

    • tau
      (hour, tau_bins)
      float64
      0.02709 0.05733 ... 0.05745 0.09308

      array([[0.0270877 , 0.05732647, 0.09203958],
             [0.02680315, 0.05759896, 0.09214987],
             [0.0266931 , 0.05764483, 0.09244683],
             [0.02699333, 0.05713952, 0.09263131],
             [0.02688743, 0.0573335 , 0.09261309],
             [0.02682686, 0.05738899, 0.0923755 ],
             [0.02736078, 0.05730989, 0.09163058],
             [0.02709459, 0.05719675, 0.09233963],
             [0.0260143 , 0.05757715, 0.09312405],
             [0.02643325, 0.05748537, 0.09276128],
             [0.02604512, 0.05695688, 0.09281864],
             [0.02653221, 0.05750187, 0.09254788],
             [0.02541264, 0.05698433, 0.09270642],
             [0.0258142 , 0.0573662 , 0.09281572],
             [0.02577848, 0.05747551, 0.09283653],
             [0.02582663, 0.05766199, 0.09305366],
             [0.0262083 , 0.05818581, 0.09442765],
             [0.02586172, 0.05809102, 0.0939246 ],
             [0.02600226, 0.05773976, 0.09318213],
             [0.02695185, 0.05763241, 0.09303027],
             [0.02669914, 0.05756156, 0.09276316],
             [0.02679048, 0.05723235, 0.09331579],
             [0.02705673, 0.05729915, 0.09280436],
             [0.02745168, 0.05745369, 0.09308222]])

  • Attributes: (10)

    CREATION_DATE :

    23:26 24-FEB-2021

    Data_Source :

    Global Tropical Moored Buoy Array Project Office/NOAA/PMEL

    File_info :

    Contact: Dai.C.McClurg@noaa.gov

    Request_for_acknowledgement :

    If you use these data in publications or presentations, please acknowledge the GTMBA Project Office of NOAA/PMEL. Also, we would appreciate receiving a preprint and/or reprint of publications utilizing the data for inclusion in our bibliography. Relevant publications should be sent to: GTMBA Project Office, NOAA/Pacific Marine Environmental Laboratory, 7600 Sand Point Way NE, Seattle, WA 98115

    _FillValue :

    1.0000000409184788e+35

    array :

    TAO/TRITON

    missing_value :

    1.0000000409184788e+35

    platform_code :

    0n165e

    site_code :

    0n165e

    wmo_platform_code :

    52321

  <xarray.DatasetView>
  Dimensions:   (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth     (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      xh        float64 -140.0
      yh        float64 0.0625
      yq        float64 -0.0625
    * hour      (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23
    * tau_bins  (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT        (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0006551
      eps       (depth, hour, tau_bins) float32 3.001e-08 1.736e-07 ... 3.2e-08
      chi       (depth, hour, tau_bins) float32 3.153e-08 5.147e-08 ... 5.686e-09
      Jb        (depth, hour, tau_bins) float32 3.583e-10 4.566e-10 ... 4.18e-10
      Jq        (depth, hour, tau_bins) float64 -0.5587 -0.7393 ... -28.37 -5.81
      S2        (depth, hour, tau_bins) float32 0.000118 0.0002866 ... 2.19e-05
      N2        (depth, hour, tau_bins) float32 0.0002707 0.0002679 ... 1.066e-05
      Rig       (depth, hour, tau_bins) float32 1.866 0.6854 ... 0.4224 0.5125
      Rig_T     (depth, hour, tau_bins) float32 1.75 0.6778 0.516 ... 0.2708 0.353
      tau       (hour, tau_bins) float32 0.0295 0.054 0.08233 ... 0.05439 0.08311
  Attributes:
      title:    baseline

  baseline.001

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      cartesian_axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-89., -69., -59., -49., -39., -29.])

    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float32
      1.001e-06 1.001e-06 ... 0.0006551

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      standard_name :

      ocean_vertical_heat_diffusivity

      array([[[1.00062630e-06, 1.00062675e-06, 1.00062948e-06],
              [1.00062630e-06, 1.00062675e-06, 1.00062914e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00070815e-06],
              [1.00062630e-06, 1.00062675e-06, 1.00062823e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062800e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062800e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062812e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062857e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062869e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062857e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00570742e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00063801e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00505042e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00063016e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062857e-06],
              [1.00062630e-06, 1.00062653e-06, 1.00062778e-06],
              [1.00062630e-06, 1.00062664e-06, 1.00062721e-06],
              [1.00062630e-06, 1.00062653e-06, 1.00062732e-06],
              [1.00062630e-06, 1.00062653e-06, 1.00062744e-06],
              [1.00062618e-06, 1.00062664e-06, 1.00062789e-06],
      ...
              [1.31017505e-03, 1.84910290e-03, 3.04656965e-03],
              [1.20016653e-03, 1.93014904e-03, 2.43997667e-02],
              [1.26303639e-03, 2.55707884e-03, 3.35960016e-02],
              [1.21755805e-03, 3.45059624e-03, 4.70925234e-02],
              [1.20411417e-03, 6.63228473e-03, 5.48477545e-02],
              [1.20652630e-03, 1.19018322e-02, 6.12227395e-02],
              [1.33580097e-03, 1.68863572e-02, 6.64590597e-02],
              [1.38179970e-03, 2.08421741e-02, 7.17242733e-02],
              [1.61000830e-03, 2.49606222e-02, 7.40951598e-02],
              [1.71344716e-03, 2.64077000e-02, 7.43862391e-02],
              [1.82845863e-03, 2.77032442e-02, 7.27289319e-02],
              [1.99175603e-03, 2.91718896e-02, 7.13082328e-02],
              [1.64016616e-03, 1.40310498e-02, 5.11850938e-02],
              [2.00258894e-03, 2.12116283e-03, 1.37765761e-02],
              [1.67123857e-03, 1.55570242e-03, 4.48137894e-03],
              [1.43854087e-03, 6.79666526e-04, 3.39539582e-03],
              [2.04196898e-03, 1.61034847e-03, 7.48192775e-04],
              [2.00651051e-03, 1.96600612e-03, 5.42554015e-04],
              [1.92917790e-03, 1.86887372e-03, 4.04946972e-04],
              [1.80337648e-03, 1.76201062e-03, 6.55124430e-04]]], dtype=float32)

    • eps
      (depth, hour, tau_bins)
      float32
      3.001e-08 1.736e-07 ... 3.2e-08

      long_name :

      $ε$

      units :

      W/kg

      array([[[3.00083727e-08, 1.73550305e-07, 3.20981485e-07],
              [2.98638838e-08, 1.78816521e-07, 3.11246595e-07],
              [3.06492360e-08, 1.81410115e-07, 3.16398513e-07],
              [3.16908242e-08, 1.81652055e-07, 2.98138332e-07],
              [3.11788320e-08, 1.74377433e-07, 2.96343785e-07],
              [2.98984233e-08, 1.70588990e-07, 3.04399180e-07],
              [2.91050224e-08, 1.61966597e-07, 2.92950801e-07],
              [2.95839531e-08, 1.59746861e-07, 2.87097237e-07],
              [3.12666408e-08, 1.56512073e-07, 2.81551166e-07],
              [3.21704050e-08, 1.55229969e-07, 2.67078150e-07],
              [3.19440652e-08, 1.50670360e-07, 2.74645004e-07],
              [3.20376188e-08, 1.50261016e-07, 2.74898809e-07],
              [3.24038005e-08, 1.48318989e-07, 2.63479620e-07],
              [3.08092964e-08, 1.40655715e-07, 2.57241481e-07],
              [2.95377269e-08, 1.37034775e-07, 2.56086707e-07],
              [2.93052338e-08, 1.29154699e-07, 2.41482866e-07],
              [2.80925558e-08, 1.29672216e-07, 2.39818291e-07],
              [2.65684719e-08, 1.28819224e-07, 2.57427473e-07],
              [2.40848834e-08, 1.30246178e-07, 2.71984561e-07],
              [2.53702339e-08, 1.34602928e-07, 3.02511069e-07],
      ...
              [1.38809497e-07, 1.66526718e-07, 3.92258016e-07],
              [1.36270828e-07, 4.17841648e-07, 8.56405109e-07],
              [1.37541633e-07, 4.53750602e-07, 7.68512791e-07],
              [1.45518257e-07, 6.34355501e-07, 7.04753916e-07],
              [1.56007019e-07, 6.35820129e-07, 5.65314110e-07],
              [1.71676305e-07, 5.92261927e-07, 4.78777906e-07],
              [1.90442833e-07, 5.49946435e-07, 4.06831958e-07],
              [2.16033243e-07, 4.86720182e-07, 3.49111531e-07],
              [2.34895424e-07, 4.21473487e-07, 3.10066298e-07],
              [2.38446120e-07, 3.72554325e-07, 2.76426590e-07],
              [2.39987941e-07, 3.42585878e-07, 2.71347915e-07],
              [2.41708932e-07, 3.12257924e-07, 2.63682296e-07],
              [2.09086110e-07, 1.89499161e-07, 1.88648585e-07],
              [2.43611026e-07, 9.61760520e-08, 7.58583596e-08],
              [1.93665912e-07, 1.17051783e-07, 4.17802113e-08],
              [1.65643399e-07, 7.42967785e-08, 3.08687760e-08],
              [2.20749484e-07, 1.22858239e-07, 2.07973976e-08],
              [2.19819796e-07, 1.34363518e-07, 2.31354473e-08],
              [2.02686465e-07, 1.25603549e-07, 2.58839439e-08],
              [1.85131938e-07, 1.17201175e-07, 3.20020845e-08]]], dtype=float32)

    • chi
      (depth, hour, tau_bins)
      float32
      3.153e-08 5.147e-08 ... 5.686e-09

      long_name :

      $χ$

      units :

      C^2/s

      array([[[3.15311368e-08, 5.14684650e-08, 9.00696406e-08],
              [3.15410986e-08, 5.30324087e-08, 9.01792561e-08],
              [3.15075219e-08, 5.37205800e-08, 9.82160913e-08],
              [3.20624061e-08, 5.38295062e-08, 9.03460275e-08],
              [3.18296536e-08, 5.18737444e-08, 9.16030842e-08],
              [3.06467669e-08, 5.12408036e-08, 9.39065075e-08],
              [3.04877297e-08, 4.99954069e-08, 8.79619648e-08],
              [3.04382226e-08, 4.94467329e-08, 9.59539150e-08],
              [3.07606456e-08, 4.88297900e-08, 9.66301599e-08],
              [3.07989403e-08, 4.88755845e-08, 9.55644310e-08],
              [3.07602761e-08, 4.79528133e-08, 9.98426657e-08],
              [3.08518295e-08, 4.82226348e-08, 9.68349596e-08],
              [3.17893125e-08, 4.86282445e-08, 9.36632176e-08],
              [3.16449871e-08, 4.65035299e-08, 9.43617451e-08],
              [3.08822727e-08, 4.60562504e-08, 9.55800061e-08],
              [3.11754427e-08, 4.47915767e-08, 8.63022080e-08],
              [3.06896979e-08, 4.50557103e-08, 8.33114768e-08],
              [3.02294971e-08, 4.45752235e-08, 8.25163937e-08],
              [2.99401677e-08, 4.39887842e-08, 8.17240959e-08],
              [3.01301455e-08, 4.42664927e-08, 8.81232864e-08],
      ...
              [4.69678980e-08, 7.31207379e-08, 1.80065271e-07],
              [4.44437944e-08, 1.60526312e-07, 5.26232157e-07],
              [4.59130831e-08, 2.16911602e-07, 4.20312915e-07],
              [4.35025953e-08, 2.93830965e-07, 3.34603868e-07],
              [4.81800519e-08, 2.92471725e-07, 2.25865008e-07],
              [5.43300516e-08, 2.66074068e-07, 1.74853156e-07],
              [6.02832841e-08, 2.26358566e-07, 1.33450698e-07],
              [6.76159573e-08, 1.83526524e-07, 1.02248393e-07],
              [7.15624537e-08, 1.46292123e-07, 8.52350865e-08],
              [6.93415956e-08, 1.20850444e-07, 7.18475022e-08],
              [6.45878373e-08, 1.03103801e-07, 6.89958028e-08],
              [6.24963974e-08, 8.90549074e-08, 6.63747244e-08],
              [3.71018416e-08, 3.94645703e-08, 4.55000446e-08],
              [6.99339324e-08, 8.64094130e-09, 1.39391068e-08],
              [5.24933412e-08, 9.25000787e-09, 4.61419702e-09],
              [5.24419441e-08, 6.12304518e-09, 5.45677326e-09],
              [1.02914335e-07, 2.33580533e-08, 2.31011832e-09],
              [1.05326329e-07, 3.94027495e-08, 2.59428723e-09],
              [9.97600011e-08, 4.49156161e-08, 3.48193008e-09],
              [8.96089247e-08, 4.73158046e-08, 5.68561020e-09]]], dtype=float32)

    • Jb
      (depth, hour, tau_bins)
      float32
      3.583e-10 4.566e-10 ... 4.18e-10

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      array([[[3.58277630e-10, 4.56598898e-10, 6.36808883e-10],
              [3.61272345e-10, 4.61960303e-10, 6.18004647e-10],
              [3.62497338e-10, 4.62224758e-10, 6.27580043e-10],
              [3.65806746e-10, 4.59576710e-10, 6.01129591e-10],
              [3.65918185e-10, 4.55617932e-10, 6.07656758e-10],
              [3.62261526e-10, 4.51856774e-10, 6.12711770e-10],
              [3.61771529e-10, 4.47667459e-10, 6.07582207e-10],
              [3.55180552e-10, 4.47039850e-10, 6.31304398e-10],
              [3.58695351e-10, 4.45621262e-10, 6.39133413e-10],
              [3.58532593e-10, 4.43890119e-10, 6.39311493e-10],
              [3.59802993e-10, 4.41411629e-10, 6.60119959e-10],
              [3.58949426e-10, 4.43239168e-10, 6.37624564e-10],
              [3.60242253e-10, 4.42435810e-10, 6.33906649e-10],
              [3.61484953e-10, 4.32591463e-10, 6.34564401e-10],
              [3.57910229e-10, 4.29199953e-10, 6.46178833e-10],
              [3.57660401e-10, 4.23346747e-10, 6.13737727e-10],
              [3.55238977e-10, 4.25607383e-10, 5.97280669e-10],
              [3.52947865e-10, 4.24758007e-10, 5.87040860e-10],
              [3.49784535e-10, 4.23031971e-10, 5.85980042e-10],
              [3.53015478e-10, 4.23206359e-10, 6.03543548e-10],
      ...
              [5.55786572e-09, 9.70253211e-09, 3.75396709e-08],
              [5.11393239e-09, 1.35741001e-08, 1.60814352e-07],
              [5.44395062e-09, 2.43575133e-08, 1.45002247e-07],
              [4.86894791e-09, 4.12963530e-08, 1.47366620e-07],
              [5.39292611e-09, 5.72370809e-08, 1.25824272e-07],
              [6.41620401e-09, 6.45678071e-08, 1.10190449e-07],
              [6.78160905e-09, 6.84167958e-08, 9.38289020e-08],
              [7.29576932e-09, 6.47739071e-08, 7.68193615e-08],
              [8.18336510e-09, 5.81149209e-08, 7.04859318e-08],
              [7.76166331e-09, 5.19686871e-08, 6.43213554e-08],
              [7.04196568e-09, 4.70384727e-08, 6.16404350e-08],
              [7.90036125e-09, 4.03301179e-08, 6.00140240e-08],
              [4.88817342e-09, 1.86461691e-08, 3.97448545e-08],
              [7.09699721e-09, 1.72511705e-09, 1.00553397e-08],
              [4.06853129e-09, 1.28853817e-09, 2.58423283e-09],
              [2.98437519e-09, 1.13034027e-09, 3.60601060e-09],
              [1.09980309e-08, 2.05348050e-09, 5.57511981e-10],
              [1.15927854e-08, 4.67873607e-09, 3.97649857e-10],
              [1.14538530e-08, 5.87054716e-09, 3.38541611e-10],
              [1.07085452e-08, 6.21498941e-09, 4.18006518e-10]]], dtype=float32)

    • Jq
      (depth, hour, tau_bins)
      float64
      -0.5587 -0.7393 ... -28.37 -5.81

      units :

      W/m^2

      long_name :

      $J_q^t$

      array([[[  -0.55872717,   -0.73933429,   -1.01263507],
              [  -0.55909885,   -0.74781458,   -1.02032381],
              [  -0.56339972,   -0.75235563,   -1.04114909],
              [  -0.56574466,   -0.75408049,   -1.00271978],
              [  -0.56327974,   -0.74221028,   -0.9980942 ],
              [  -0.55951453,   -0.73760955,   -1.01441338],
              [  -0.55592129,   -0.73105519,   -0.98191639],
              [  -0.55362725,   -0.72750384,   -1.02130405],
              [  -0.56301865,   -0.7252834 ,   -1.01789328],
              [  -0.55946494,   -0.71693061,   -1.03545581],
              [  -0.55886687,   -0.71391623,   -1.07142686],
              [  -0.55816062,   -0.71663231,   -1.04043361],
              [  -0.56094336,   -0.71781622,   -1.06883406],
              [  -0.55902317,   -0.70272396,   -1.05033251],
              [  -0.55030415,   -0.69910558,   -1.06303512],
              [  -0.55435328,   -0.68816715,   -0.99056348],
              [  -0.54662938,   -0.68960124,   -0.96040917],
              [  -0.5458248 ,   -0.68627143,   -0.95080166],
              [  -0.54284984,   -0.67667759,   -0.94810626],
              [  -0.54519989,   -0.67693639,   -0.979253  ],
      ...
              [ -22.76061424,  -33.70290482,  -94.29752641],
              [ -20.68569739,  -42.29942   , -335.72166172],
              [ -21.75314978,  -72.79134959, -352.68323712],
              [ -20.33633891, -130.71658974, -353.91620765],
              [ -20.80126538, -172.30126404, -320.39020802],
              [ -22.98944112, -190.17481245, -293.028654  ],
              [ -25.53994451, -196.43233475, -267.77275736],
              [ -28.77743047, -187.48167067, -249.95806384],
              [ -32.94550126, -175.95360989, -226.59814176],
              [ -33.82984713, -164.83311868, -216.09387751],
              [ -33.77147363, -156.69712408, -208.27023105],
              [ -35.7552922 , -146.57650858, -204.76277986],
              [ -25.48603855,  -75.301242  , -140.61098295],
              [ -33.06930083,  -12.40822579,  -42.20722391],
              [ -26.257377  ,  -10.45363752,  -15.4468764 ],
              [ -24.46266471,   -5.90238543,  -13.88000668],
              [ -40.2315518 ,  -17.05878402,   -4.50023447],
              [ -42.74567498,  -27.01418861,   -3.93155364],
              [ -40.26200269,  -28.88526491,   -3.4606702 ],
              [ -36.62022982,  -28.36562722,   -5.8097894 ]]])

    • S2
      (depth, hour, tau_bins)
      float32
      0.000118 0.0002866 ... 2.19e-05

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([[[1.18035634e-04, 2.86617229e-04, 3.68310226e-04],
              [1.19055607e-04, 2.93288234e-04, 3.68738431e-04],
              [1.21223944e-04, 2.90943921e-04, 3.86649830e-04],
              [1.24606799e-04, 2.89873598e-04, 4.03484708e-04],
              [1.22047291e-04, 2.80318171e-04, 4.00653458e-04],
              [1.19750825e-04, 2.80132226e-04, 3.87836946e-04],
              [1.17275791e-04, 2.74971506e-04, 3.79573670e-04],
              [1.19338751e-04, 2.72942707e-04, 3.62854218e-04],
              [1.24937898e-04, 2.71977799e-04, 3.54683434e-04],
              [1.28315500e-04, 2.71855562e-04, 3.48416914e-04],
              [1.28547457e-04, 2.69131095e-04, 3.54306278e-04],
              [1.29285705e-04, 2.68015225e-04, 3.56286910e-04],
              [1.28866639e-04, 2.66737159e-04, 3.46874818e-04],
              [1.24962899e-04, 2.58250860e-04, 3.61833983e-04],
              [1.19088931e-04, 2.55677296e-04, 3.59144819e-04],
              [1.16096089e-04, 2.48583849e-04, 3.58901307e-04],
              [1.14474693e-04, 2.44785100e-04, 3.55322234e-04],
              [1.08996122e-04, 2.44438765e-04, 3.55026539e-04],
              [1.02533719e-04, 2.44985917e-04, 3.44370579e-04],
              [1.07392145e-04, 2.47505552e-04, 3.61505779e-04],
      ...
              [7.28670711e-05, 6.50857328e-05, 6.72916212e-05],
              [7.45321959e-05, 8.67422787e-05, 4.64802906e-05],
              [7.49734172e-05, 9.21883038e-05, 2.75155944e-05],
              [7.82151910e-05, 9.42244806e-05, 1.68252045e-05],
              [8.51124059e-05, 7.84320728e-05, 1.13622445e-05],
              [8.91593227e-05, 6.00015410e-05, 8.76838749e-06],
              [9.46019863e-05, 4.17266747e-05, 6.79051436e-06],
              [9.57649972e-05, 2.93372104e-05, 5.51613903e-06],
              [9.58165911e-05, 2.10807702e-05, 4.71648173e-06],
              [9.64381688e-05, 1.72020154e-05, 4.28609746e-06],
              [9.73792412e-05, 1.46298644e-05, 4.15710019e-06],
              [9.31051254e-05, 1.27367175e-05, 4.07165271e-06],
              [1.01596459e-04, 1.21337544e-05, 3.59626006e-06],
              [1.06722146e-04, 3.36617632e-05, 3.80803567e-06],
              [1.02554128e-04, 4.74796179e-05, 5.74421392e-06],
              [9.62065242e-05, 5.39669963e-05, 6.05823743e-06],
              [9.68023087e-05, 5.40903638e-05, 8.59525062e-06],
              [9.29079615e-05, 5.20505928e-05, 1.37420657e-05],
              [8.81354208e-05, 4.90190614e-05, 1.74025445e-05],
              [8.50617798e-05, 4.72261891e-05, 2.19031808e-05]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float32
      0.0002707 0.0002679 ... 1.066e-05

      long_name :

      $N^2$

      units :

      s$^{-2}$

      array([[[2.70653982e-04, 2.67854164e-04, 2.60556408e-04],
              [2.70489603e-04, 2.67931056e-04, 2.61637673e-04],
              [2.68508651e-04, 2.68075935e-04, 2.64729460e-04],
              [2.66491756e-04, 2.67974130e-04, 2.71308294e-04],
              [2.67325930e-04, 2.67938973e-04, 2.70344783e-04],
              [2.69618089e-04, 2.67519441e-04, 2.64699716e-04],
              [2.69445707e-04, 2.67475261e-04, 2.66230258e-04],
              [2.67186144e-04, 2.68850679e-04, 2.58856395e-04],
              [2.71684374e-04, 2.66605173e-04, 2.58436747e-04],
              [2.69634824e-04, 2.68864445e-04, 2.49675912e-04],
              [2.70803575e-04, 2.66544346e-04, 2.50487588e-04],
              [2.71231111e-04, 2.66110990e-04, 2.51161167e-04],
              [2.71857542e-04, 2.66223098e-04, 2.52874015e-04],
              [2.72617326e-04, 2.63849157e-04, 2.66338582e-04],
              [2.71545228e-04, 2.64220784e-04, 2.70796998e-04],
              [2.70907389e-04, 2.63898313e-04, 2.71208119e-04],
              [2.73490848e-04, 2.62059155e-04, 2.72291130e-04],
              [2.73298821e-04, 2.62513553e-04, 2.72275705e-04],
              [2.71756551e-04, 2.64893140e-04, 2.73101410e-04],
              [2.73922691e-04, 2.64201226e-04, 2.70985212e-04],
      ...
              [2.66783991e-05, 2.24435680e-05, 2.01482108e-05],
              [2.76282153e-05, 2.52834870e-05, 1.53130677e-05],
              [2.78609550e-05, 2.53739199e-05, 1.07697870e-05],
              [2.88161682e-05, 2.38241410e-05, 7.61099909e-06],
              [3.00592146e-05, 1.97585177e-05, 5.86610713e-06],
              [3.10059622e-05, 1.59887131e-05, 5.00141596e-06],
              [3.13461569e-05, 1.26825598e-05, 4.25584130e-06],
              [3.19165847e-05, 9.92587957e-06, 3.65537994e-06],
              [3.14851823e-05, 7.92554147e-06, 3.33652520e-06],
              [3.05563590e-05, 6.80374478e-06, 3.12953216e-06],
              [3.05634167e-05, 6.46581339e-06, 3.12648399e-06],
              [3.00894790e-05, 5.96192513e-06, 3.03067145e-06],
              [3.18050443e-05, 5.42599446e-06, 3.01429736e-06],
              [3.27169000e-05, 9.65514482e-06, 2.92601317e-06],
              [3.30679140e-05, 1.31469769e-05, 3.74685192e-06],
              [3.10162686e-05, 1.42103781e-05, 4.23103938e-06],
              [3.14569043e-05, 1.57434079e-05, 5.41504414e-06],
              [3.13854980e-05, 1.62794604e-05, 7.40211590e-06],
              [3.05814792e-05, 1.66734153e-05, 9.08484435e-06],
              [2.97653733e-05, 1.71468819e-05, 1.06635689e-05]]], dtype=float32)

    • Rig
      (depth, hour, tau_bins)
      float32
      1.866 0.6854 ... 0.4224 0.5125

      long_name :

      $Ri^g$

      array([[[1.8662848 , 0.68544173, 0.5350752 ],
              [1.8600645 , 0.6764416 , 0.52997637],
              [1.8082513 , 0.6684065 , 0.51994437],
              [1.7514868 , 0.66514564, 0.53821963],
              [1.7742116 , 0.6801465 , 0.5362674 ],
              [1.8050451 , 0.6883631 , 0.5284693 ],
              [1.826543  , 0.6967864 , 0.53718793],
              [1.839041  , 0.69765747, 0.538539  ],
              [1.8039743 , 0.69942325, 0.54343367],
              [1.762919  , 0.7034895 , 0.5462879 ],
              [1.7395374 , 0.71042335, 0.5473652 ],
              [1.743981  , 0.7068553 , 0.5498181 ],
              [1.7485907 , 0.7094732 , 0.5552983 ],
              [1.800962  , 0.7201221 , 0.56303406],
              [1.8770511 , 0.7300272 , 0.5768663 ],
              [1.8958896 , 0.7441837 , 0.59068596],
              [1.9319459 , 0.7465268 , 0.6032988 ],
              [1.9969598 , 0.76601106, 0.59619606],
              [2.1091611 , 0.77056324, 0.59924066],
              [2.072525  , 0.76344836, 0.5768527 ],
      ...
              [0.417879  , 0.3804391 , 0.3238179 ],
              [0.41090554, 0.30941188, 0.3488606 ],
              [0.403327  , 0.30385834, 0.392285  ],
              [0.39265534, 0.28617096, 0.46646762],
              [0.36620998, 0.2877478 , 0.5247332 ],
              [0.34303582, 0.31285378, 0.5649361 ],
              [0.3227075 , 0.34359443, 0.61189747],
              [0.3142567 , 0.38431424, 0.65183944],
              [0.30991292, 0.41828164, 0.6832897 ],
              [0.31734043, 0.44741976, 0.71992517],
              [0.32099736, 0.47542647, 0.747839  ],
              [0.33062443, 0.4994597 , 0.74357045],
              [0.32144618, 0.5118849 , 0.80607224],
              [0.31450492, 0.352032  , 0.76655614],
              [0.33332175, 0.34234598, 0.6389044 ],
              [0.35182685, 0.34710622, 0.6787852 ],
              [0.3533942 , 0.3665613 , 0.63400173],
              [0.36926734, 0.38762894, 0.5510011 ],
              [0.38271207, 0.4089583 , 0.52773184],
              [0.39180535, 0.42236108, 0.5125109 ]]], dtype=float32)

    • Rig_T
      (depth, hour, tau_bins)
      float32
      1.75 0.6778 0.516 ... 0.2708 0.353

      long_name :

      $Ri^g_T$

      array([[[1.7498139 , 0.6778394 , 0.5160031 ],
              [1.7240694 , 0.6695879 , 0.49940598],
              [1.6583761 , 0.6628067 , 0.498627  ],
              [1.6018262 , 0.658349  , 0.51457024],
              [1.6139219 , 0.66992456, 0.52054894],
              [1.6932245 , 0.6747377 , 0.5110692 ],
              [1.7320414 , 0.68135345, 0.5248196 ],
              [1.7112534 , 0.6885524 , 0.52049387],
              [1.6603553 , 0.69147515, 0.5276542 ],
              [1.6072863 , 0.6940439 , 0.542287  ],
              [1.5864432 , 0.70075834, 0.5364443 ],
              [1.586266  , 0.70577735, 0.54150844],
              [1.5927796 , 0.70689046, 0.5504924 ],
              [1.6602877 , 0.7092931 , 0.55996585],
              [1.7805405 , 0.7184469 , 0.5745784 ],
              [1.8147937 , 0.7288473 , 0.58771074],
              [1.8759367 , 0.7344651 , 0.59955335],
              [1.9077746 , 0.7484478 , 0.59346616],
              [1.9881756 , 0.7515857 , 0.5964693 ],
              [1.9673991 , 0.7486092 , 0.56754345],
      ...
              [0.2634116 , 0.2507721 , 0.24641225],
              [0.2607986 , 0.21899809, 0.26634485],
              [0.25649   , 0.2143133 , 0.2940078 ],
              [0.25049907, 0.20534892, 0.32926372],
              [0.23761362, 0.20744732, 0.35544658],
              [0.22342424, 0.21846932, 0.37724414],
              [0.21559256, 0.2321828 , 0.39727533],
              [0.21003583, 0.2461529 , 0.41424218],
              [0.21187592, 0.26617616, 0.4138385 ],
              [0.21543066, 0.27246305, 0.436479  ],
              [0.2142856 , 0.27827302, 0.4584359 ],
              [0.2159077 , 0.29173055, 0.43860143],
              [0.20547742, 0.29568854, 0.4857257 ],
              [0.21092497, 0.21182545, 0.4756171 ],
              [0.22476283, 0.2046566 , 0.37278977],
              [0.23309311, 0.20733707, 0.4022013 ],
              [0.23358554, 0.22945035, 0.39872727],
              [0.24027762, 0.24394931, 0.36718082],
              [0.24860656, 0.26064178, 0.36847693],
              [0.25379637, 0.27075374, 0.35295993]]], dtype=float32)

    • tau
      (hour, tau_bins)
      float32
      0.0295 0.054 ... 0.05439 0.08311

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([[0.0295027 , 0.05400424, 0.08233272],
             [0.029748  , 0.0539082 , 0.08225691],
             [0.02973902, 0.05381709, 0.08261511],
             [0.02958953, 0.05378674, 0.08290136],
             [0.02952781, 0.0541986 , 0.08320552],
             [0.02957567, 0.05422445, 0.08379569],
             [0.02954055, 0.05467816, 0.08413152],
             [0.02949499, 0.05448272, 0.08375749],
             [0.02946378, 0.05434905, 0.08394575],
             [0.02955062, 0.05434605, 0.08510967],
             [0.02943835, 0.05417863, 0.08425807],
             [0.02927817, 0.05410217, 0.08441167],
             [0.02894278, 0.05420254, 0.08438533],
             [0.02929234, 0.05503666, 0.08478092],
             [0.0293923 , 0.05574505, 0.08499894],
             [0.0296241 , 0.05632416, 0.08501871],
             [0.03005047, 0.05656263, 0.08522117],
             [0.03020069, 0.05679295, 0.08566844],
             [0.03010707, 0.05716396, 0.08595885],
             [0.02992886, 0.05671743, 0.08496676],
             [0.03006576, 0.05620772, 0.08470601],
             [0.02999885, 0.05570246, 0.08516616],
             [0.02975408, 0.05507209, 0.08390015],
             [0.02965912, 0.05439087, 0.0831105 ]], dtype=float32)

  • Attributes: (1)

    title :

    baseline

  <xarray.DatasetView>
  Dimensions:   (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth     (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      xh        float64 -140.0
      yh        float64 0.0625
      yq        float64 -0.0625
    * hour      (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23
    * tau_bins  (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT        (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0006196
      eps       (depth, hour, tau_bins) float32 3.571e-09 7.384e-09 ... 4.795e-08
      chi       (depth, hour, tau_bins) float32 1.417e-08 1.12e-08 ... 1.021e-08
      Jb        (depth, hour, tau_bins) float32 2.363e-10 2.037e-10 ... 8.176e-10
      Jq        (depth, hour, tau_bins) float64 -0.3639 -0.3232 ... -34.57 -7.167
      S2        (depth, hour, tau_bins) float32 1.608e-05 3.475e-05 ... 4.999e-05
      N2        (depth, hour, tau_bins) float32 0.0002237 0.0002014 ... 1.187e-05
      Rig       (depth, hour, tau_bins) float32 13.2 4.297 3.056 ... 0.2232 0.2512
      Rig_T     (depth, hour, tau_bins) float32 13.39 4.173 ... 0.1836 0.2279
      tau       (hour, tau_bins) float32 0.02931 0.05352 0.0824 ... 0.0542 0.08266
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  baseline.kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      cartesian_axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-89., -69., -59., -49., -39., -29.])

    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float32
      1.001e-06 1.001e-06 ... 0.0006196

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062664e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062664e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
              [1.00062641e-06, 1.00062653e-06, 1.00062653e-06],
      ...
              [8.69949930e-04, 9.59244964e-04, 1.11343141e-03],
              [8.50179931e-04, 1.02828699e-03, 1.24498047e-02],
              [8.22628965e-04, 1.05729094e-03, 3.77871096e-03],
              [8.18862813e-04, 1.26605667e-03, 2.33795643e-02],
              [8.30616336e-04, 1.36252248e-03, 3.00153233e-02],
              [8.26583593e-04, 1.66581431e-03, 3.50915715e-02],
              [8.35646526e-04, 2.89651891e-03, 4.03600074e-02],
              [8.40844936e-04, 5.73793054e-03, 4.29606661e-02],
              [8.83748115e-04, 8.86736996e-03, 4.64188121e-02],
              [8.89282324e-04, 1.00382138e-02, 4.53674309e-02],
              [9.06878675e-04, 1.16374400e-02, 4.41440344e-02],
              [9.52538336e-04, 1.18752746e-02, 4.22920287e-02],
              [9.77167627e-04, 3.11270822e-03, 2.84510609e-02],
              [1.07384217e-03, 1.43216818e-03, 3.58461030e-03],
              [9.83901555e-04, 1.30531006e-03, 1.26952492e-03],
              [9.94352158e-04, 8.17729277e-04, 1.27175078e-03],
              [1.12384127e-03, 1.34078134e-03, 5.45061426e-04],
              [1.12999673e-03, 1.29678287e-03, 6.57544471e-04],
              [1.10153994e-03, 1.23138377e-03, 6.13115670e-04],
              [1.06722605e-03, 1.15680229e-03, 6.19593775e-04]]], dtype=float32)

    • eps
      (depth, hour, tau_bins)
      float32
      3.571e-09 7.384e-09 ... 4.795e-08

      long_name :

      $ε$

      units :

      W/kg

      array([[[3.57124752e-09, 7.38426387e-09, 1.14503083e-08],
              [3.46380302e-09, 7.70825714e-09, 1.21308972e-08],
              [3.55494123e-09, 7.66528174e-09, 1.25291590e-08],
              [3.64194630e-09, 7.73805198e-09, 1.09109886e-08],
              [3.66173114e-09, 7.54748086e-09, 1.32884317e-08],
              [3.56473562e-09, 7.45457740e-09, 1.38303182e-08],
              [3.53924134e-09, 7.21436511e-09, 1.41857326e-08],
              [3.55269147e-09, 7.17422743e-09, 1.52570259e-08],
              [3.60691987e-09, 7.19619075e-09, 1.58858029e-08],
              [3.62712704e-09, 6.97547353e-09, 1.70368057e-08],
              [3.57896757e-09, 7.23481275e-09, 1.65586016e-08],
              [3.63070085e-09, 7.11863546e-09, 1.52314037e-08],
              [3.65208352e-09, 7.28103622e-09, 1.54543844e-08],
              [3.59472363e-09, 7.15794535e-09, 1.41217127e-08],
              [3.48057760e-09, 7.03329484e-09, 1.39779228e-08],
              [3.43757622e-09, 6.78592382e-09, 1.22623325e-08],
              [3.30933392e-09, 6.82196699e-09, 1.17316370e-08],
              [3.28501937e-09, 6.64459865e-09, 1.14167511e-08],
              [3.28050898e-09, 6.34667874e-09, 1.17305454e-08],
              [3.30512262e-09, 6.53624355e-09, 1.20629728e-08],
      ...
              [1.97078904e-07, 1.75519631e-07, 2.64559191e-07],
              [1.91761814e-07, 2.66213419e-07, 1.40613520e-06],
              [1.88211999e-07, 2.84233721e-07, 8.98357257e-07],
              [1.88038371e-07, 4.99263649e-07, 1.16582873e-06],
              [1.96016416e-07, 7.13196641e-07, 1.02670094e-06],
              [2.06843083e-07, 8.00393195e-07, 9.15784995e-07],
              [2.20204754e-07, 8.53189874e-07, 7.59640102e-07],
              [2.31497069e-07, 8.06180196e-07, 6.55824294e-07],
              [2.52628126e-07, 7.68301788e-07, 5.73569423e-07],
              [2.59949047e-07, 7.08147411e-07, 5.34952221e-07],
              [2.69296123e-07, 6.53131451e-07, 4.97154474e-07],
              [2.78680091e-07, 6.14048531e-07, 4.91469621e-07],
              [2.62996366e-07, 4.09287452e-07, 3.08206154e-07],
              [2.75822629e-07, 2.59727585e-07, 1.09740199e-07],
              [2.34932443e-07, 2.82902306e-07, 7.34295185e-08],
              [2.35040972e-07, 2.03696345e-07, 5.82381858e-08],
              [2.72802367e-07, 2.68030021e-07, 3.45247777e-08],
              [2.74928141e-07, 2.46962657e-07, 4.32012790e-08],
              [2.64318601e-07, 2.23850662e-07, 4.75941953e-08],
              [2.49914194e-07, 2.03934263e-07, 4.79486744e-08]]], dtype=float32)

    • chi
      (depth, hour, tau_bins)
      float32
      1.417e-08 1.12e-08 ... 1.021e-08

      long_name :

      $χ$

      units :

      C^2/s

      array([[[1.41654599e-08, 1.12043317e-08, 1.14997345e-08],
              [1.43564414e-08, 1.10966809e-08, 1.06867253e-08],
              [1.40540273e-08, 1.12314247e-08, 1.03123279e-08],
              [1.41355176e-08, 1.13349161e-08, 1.00236583e-08],
              [1.42605980e-08, 1.10975300e-08, 1.07465459e-08],
              [1.42887382e-08, 1.11918066e-08, 1.07850138e-08],
              [1.43708263e-08, 1.12686127e-08, 1.08477582e-08],
              [1.42204337e-08, 1.12907834e-08, 1.09332676e-08],
              [1.43949102e-08, 1.13406422e-08, 1.08743325e-08],
              [1.42460177e-08, 1.14293757e-08, 1.04407292e-08],
              [1.40131853e-08, 1.15438645e-08, 1.04089919e-08],
              [1.38146916e-08, 1.15858061e-08, 1.04847828e-08],
              [1.37048985e-08, 1.15371819e-08, 1.06222746e-08],
              [1.38580338e-08, 1.15255503e-08, 1.07217994e-08],
              [1.39460585e-08, 1.15467724e-08, 1.07971161e-08],
              [1.40707135e-08, 1.14792176e-08, 1.11246070e-08],
              [1.44085774e-08, 1.13738761e-08, 1.11539817e-08],
              [1.43768464e-08, 1.14945884e-08, 1.12381482e-08],
              [1.44178420e-08, 1.15233316e-08, 1.11600116e-08],
              [1.44640957e-08, 1.12931424e-08, 1.13877565e-08],
      ...
              [1.43766911e-07, 1.03006442e-07, 8.78815598e-08],
              [1.37191023e-07, 1.83328794e-07, 7.09568042e-07],
              [1.25446263e-07, 1.67309494e-07, 3.59490400e-07],
              [1.29034063e-07, 2.71363888e-07, 5.55303927e-07],
              [1.28467946e-07, 3.13767430e-07, 4.10859371e-07],
              [1.31779615e-07, 3.50028699e-07, 3.47528783e-07],
              [1.37602086e-07, 3.64599771e-07, 2.63568069e-07],
              [1.47534877e-07, 3.30650295e-07, 2.13382762e-07],
              [1.61122401e-07, 2.85556155e-07, 1.73867647e-07],
              [1.52407182e-07, 2.44686731e-07, 1.56882791e-07],
              [1.51709003e-07, 2.20665001e-07, 1.45941712e-07],
              [1.50763512e-07, 1.98459645e-07, 1.46552651e-07],
              [1.29097174e-07, 8.24987865e-08, 7.70501956e-08],
              [1.57343763e-07, 4.65327865e-08, 1.47697543e-08],
              [1.21017209e-07, 3.88323684e-08, 6.95555213e-09],
              [1.44461794e-07, 2.23305978e-08, 6.47459286e-09],
              [1.90294784e-07, 1.18683403e-07, 3.80536269e-09],
              [2.08375866e-07, 1.24235868e-07, 7.61631114e-09],
              [2.02433256e-07, 1.21694356e-07, 1.00547233e-08],
              [1.91222796e-07, 1.17619521e-07, 1.02139150e-08]]], dtype=float32)

    • Jb
      (depth, hour, tau_bins)
      float32
      2.363e-10 2.037e-10 ... 8.176e-10

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[2.36305003e-10, 2.03665917e-10, 2.08056850e-10],
              [2.36786424e-10, 2.02221850e-10, 2.09279122e-10],
              [2.33130792e-10, 2.03391748e-10, 2.06526990e-10],
              [2.32916963e-10, 2.05656311e-10, 2.00404207e-10],
              [2.33894487e-10, 2.05576237e-10, 2.02458952e-10],
              [2.35067965e-10, 2.06578477e-10, 2.01200598e-10],
              [2.36723557e-10, 2.07841605e-10, 1.97894201e-10],
              [2.34675418e-10, 2.07456149e-10, 2.04126174e-10],
              [2.35204634e-10, 2.06371822e-10, 2.01659162e-10],
              [2.33386255e-10, 2.09510145e-10, 1.95423525e-10],
              [2.32901309e-10, 2.10196471e-10, 1.95556377e-10],
              [2.29793545e-10, 2.10664777e-10, 1.96119843e-10],
              [2.28470382e-10, 2.09507939e-10, 1.99778888e-10],
              [2.31024630e-10, 2.09797971e-10, 1.99606068e-10],
              [2.35091085e-10, 2.09259596e-10, 1.99032207e-10],
              [2.39884834e-10, 2.09699591e-10, 2.02228262e-10],
              [2.45257092e-10, 2.07174472e-10, 2.03287082e-10],
              [2.45073906e-10, 2.07818360e-10, 2.04217240e-10],
              [2.46258847e-10, 2.07750095e-10, 2.05482159e-10],
              [2.46289100e-10, 2.06136733e-10, 2.08115400e-10],
      ...
              [1.10021112e-08, 1.07078728e-08, 9.40200451e-09],
              [1.06592415e-08, 1.59663145e-08, 1.32835780e-07],
              [1.03445750e-08, 1.54878776e-08, 5.95220513e-08],
              [1.09041141e-08, 2.38682460e-08, 1.59961715e-07],
              [1.09902807e-08, 3.03844914e-08, 1.49897033e-07],
              [1.09048992e-08, 3.81727965e-08, 1.61086376e-07],
              [1.12891199e-08, 5.17055732e-08, 1.47693413e-07],
              [1.13750245e-08, 5.63804861e-08, 1.32643848e-07],
              [1.32148603e-08, 6.07146688e-08, 1.20656594e-07],
              [1.23768604e-08, 5.72766012e-08, 1.13463010e-07],
              [1.22800037e-08, 5.40147020e-08, 1.14951519e-07],
              [1.20845041e-08, 4.98633597e-08, 1.05718762e-07],
              [9.85280746e-09, 2.33101858e-08, 5.91517733e-08],
              [1.16605072e-08, 4.99037567e-09, 8.04534306e-09],
              [7.44766782e-09, 3.38226780e-09, 2.08077888e-09],
              [8.68693206e-09, 1.48512147e-09, 3.18790239e-09],
              [1.40054999e-08, 9.78285009e-09, 3.67206598e-10],
              [1.44412642e-08, 1.08139648e-08, 5.36393763e-10],
              [1.42424401e-08, 1.11552581e-08, 6.93334057e-10],
              [1.40376359e-08, 1.11661604e-08, 8.17562462e-10]]], dtype=float32)

    • Jq
      (depth, hour, tau_bins)
      float64
      -0.3639 -0.3232 ... -34.57 -7.167

      units :

      W/m^2

      long_name :

      $J_q^t$

      array([[[-3.63856336e-01, -3.23210307e-01, -3.25944868e-01],
              [-3.64410742e-01, -3.22438626e-01, -3.14459372e-01],
              [-3.60407654e-01, -3.23780796e-01, -3.09333925e-01],
              [-3.60840765e-01, -3.25704081e-01, -3.03560250e-01],
              [-3.63860861e-01, -3.23031085e-01, -3.12243092e-01],
              [-3.65510015e-01, -3.23045562e-01, -3.13675629e-01],
              [-3.65869896e-01, -3.24214106e-01, -3.16109657e-01],
              [-3.64828718e-01, -3.25053940e-01, -3.17998895e-01],
              [-3.66923519e-01, -3.24808720e-01, -3.16305131e-01],
              [-3.64535768e-01, -3.27514952e-01, -3.11441584e-01],
              [-3.62548931e-01, -3.28812389e-01, -3.11511710e-01],
              [-3.59663507e-01, -3.29657957e-01, -3.14064940e-01],
              [-3.57138041e-01, -3.28465274e-01, -3.13897933e-01],
              [-3.58997834e-01, -3.27921523e-01, -3.16069328e-01],
              [-3.61749182e-01, -3.27854408e-01, -3.15479682e-01],
              [-3.61282345e-01, -3.26603508e-01, -3.20980527e-01],
              [-3.66450614e-01, -3.26124243e-01, -3.21068923e-01],
              [-3.64773864e-01, -3.27023533e-01, -3.22861464e-01],
              [-3.65693197e-01, -3.27779466e-01, -3.23091044e-01],
              [-3.66243552e-01, -3.25203886e-01, -3.25132215e-01],
      ...
              [-3.11515463e+01, -2.95920814e+01, -3.13240759e+01],
              [-2.97588069e+01, -4.01929936e+01, -3.26898750e+02],
              [-2.80524053e+01, -3.79606483e+01, -1.52565274e+02],
              [-2.87241693e+01, -5.96539726e+01, -3.31737156e+02],
              [-2.92523879e+01, -7.74188050e+01, -3.17598502e+02],
              [-2.97933349e+01, -1.03626681e+02, -3.14021997e+02],
              [-3.06915511e+01, -1.33709094e+02, -2.94227293e+02],
              [-3.16197143e+01, -1.50164779e+02, -2.75888518e+02],
              [-3.48487573e+01, -1.54185040e+02, -2.59916040e+02],
              [-3.53652229e+01, -1.47535313e+02, -2.41424957e+02],
              [-3.63852525e+01, -1.43487593e+02, -2.32847570e+02],
              [-3.87944462e+01, -1.38562537e+02, -2.20087752e+02],
              [-3.47095415e+01, -6.84364904e+01, -1.35048197e+02],
              [-3.79342051e+01, -2.57598461e+01, -2.68607238e+01],
              [-3.00724935e+01, -1.82040015e+01, -1.15180749e+01],
              [-3.35407877e+01, -9.14485579e+00, -9.79402621e+00],
              [-4.07352508e+01, -3.76753676e+01, -3.51846569e+00],
              [-4.24517449e+01, -3.83129382e+01, -5.99288931e+00],
              [-4.14190661e+01, -3.68610034e+01, -7.49174915e+00],
              [-3.99809860e+01, -3.45665587e+01, -7.16703748e+00]]])

    • S2
      (depth, hour, tau_bins)
      float32
      1.608e-05 3.475e-05 ... 4.999e-05

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([[[1.60773343e-05, 3.47473433e-05, 5.45502517e-05],
              [1.55510897e-05, 3.62657956e-05, 5.80403103e-05],
              [1.57532741e-05, 3.61047132e-05, 5.88809962e-05],
              [1.61362404e-05, 3.65621709e-05, 5.16177024e-05],
              [1.63347595e-05, 3.55188567e-05, 6.21003710e-05],
              [1.59758565e-05, 3.49079783e-05, 6.53139577e-05],
              [1.57272680e-05, 3.38322498e-05, 6.75400515e-05],
              [1.60485488e-05, 3.36952726e-05, 7.21537217e-05],
              [1.64259400e-05, 3.36227749e-05, 6.97661089e-05],
              [1.64107878e-05, 3.27744456e-05, 8.03719886e-05],
              [1.60798663e-05, 3.40344159e-05, 7.83935102e-05],
              [1.63162404e-05, 3.36289268e-05, 7.09879023e-05],
              [1.63955192e-05, 3.41706000e-05, 7.23957492e-05],
              [1.62350207e-05, 3.38442551e-05, 6.78550277e-05],
              [1.52499942e-05, 3.28904680e-05, 6.58997596e-05],
              [1.51459171e-05, 3.21342086e-05, 5.73855250e-05],
              [1.46635284e-05, 3.17903687e-05, 5.46010815e-05],
              [1.46638777e-05, 3.11573203e-05, 5.30258621e-05],
              [1.46625471e-05, 2.97258357e-05, 5.51550838e-05],
              [1.46957009e-05, 3.03876823e-05, 5.71436394e-05],
      ...
              [1.76954345e-04, 1.31229055e-04, 1.19671509e-04],
              [1.75386085e-04, 1.44285470e-04, 9.24286651e-05],
              [1.76493719e-04, 1.65989506e-04, 9.93364883e-05],
              [1.77259906e-04, 1.83417767e-04, 6.67022250e-05],
              [1.80237097e-04, 2.02479947e-04, 4.05998107e-05],
              [1.86967096e-04, 2.01381699e-04, 2.87006660e-05],
              [1.91430430e-04, 1.85397657e-04, 2.00318373e-05],
              [1.97789792e-04, 1.57587536e-04, 1.56843635e-05],
              [2.03315052e-04, 1.19843135e-04, 1.30644439e-05],
              [2.03890755e-04, 9.93930225e-05, 1.25701235e-05],
              [2.09443897e-04, 8.31915386e-05, 1.15447201e-05],
              [2.06869125e-04, 7.98302353e-05, 1.13064698e-05],
              [2.16365268e-04, 1.05084415e-04, 9.60457783e-06],
              [2.22570961e-04, 1.54111607e-04, 1.23858135e-05],
              [2.14604661e-04, 1.80870440e-04, 1.95051653e-05],
              [2.11807986e-04, 1.81155774e-04, 2.01697949e-05],
              [2.13664738e-04, 1.67245424e-04, 2.97912757e-05],
              [2.12474784e-04, 1.55046437e-04, 3.86240499e-05],
              [2.08408193e-04, 1.45126833e-04, 4.46427548e-05],
              [2.01206974e-04, 1.38201241e-04, 4.99901362e-05]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float32
      0.0002237 0.0002014 ... 1.187e-05

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      array([[[2.23716459e-04, 2.01369432e-04, 2.07364807e-04],
              [2.24670584e-04, 2.00559298e-04, 2.04587486e-04],
              [2.22044764e-04, 2.02383279e-04, 2.01458635e-04],
              [2.21417606e-04, 2.03395641e-04, 1.95091299e-04],
              [2.21755530e-04, 2.02223877e-04, 2.01670875e-04],
              [2.21897033e-04, 2.02601805e-04, 2.01380739e-04],
              [2.21933631e-04, 2.02978175e-04, 2.01497838e-04],
              [2.20619098e-04, 2.03436794e-04, 2.02596842e-04],
              [2.21749637e-04, 2.02988711e-04, 2.01417221e-04],
              [2.19857873e-04, 2.04759723e-04, 1.99296846e-04],
              [2.19346490e-04, 2.05749850e-04, 1.98707756e-04],
              [2.18359230e-04, 2.06736469e-04, 2.01719973e-04],
              [2.18257366e-04, 2.06634082e-04, 1.97994610e-04],
              [2.19672744e-04, 2.05267948e-04, 2.00647381e-04],
              [2.20484260e-04, 2.05869466e-04, 1.96984562e-04],
              [2.22277275e-04, 2.05256205e-04, 2.00871931e-04],
              [2.22674425e-04, 2.04421536e-04, 2.00914845e-04],
              [2.22843708e-04, 2.05170858e-04, 2.03752279e-04],
              [2.24134274e-04, 2.05697404e-04, 2.03576696e-04],
              [2.23726267e-04, 2.04792828e-04, 2.06141849e-04],
      ...
              [4.44578545e-05, 3.09555980e-05, 2.18656496e-05],
              [4.37544404e-05, 3.26075969e-05, 2.38331431e-05],
              [4.39932483e-05, 3.30363655e-05, 2.08300789e-05],
              [4.41018747e-05, 3.41599880e-05, 1.92035659e-05],
              [4.40224831e-05, 3.48551039e-05, 1.46976718e-05],
              [4.41208649e-05, 3.49741385e-05, 1.14134200e-05],
              [4.50448970e-05, 3.32406053e-05, 8.23586197e-06],
              [4.56341149e-05, 2.86893446e-05, 7.04849163e-06],
              [4.63023462e-05, 2.27672244e-05, 6.17078695e-06],
              [4.63085671e-05, 2.06949808e-05, 5.77945048e-06],
              [4.70361374e-05, 1.78653881e-05, 5.42836233e-06],
              [4.65924677e-05, 1.72670952e-05, 5.22179744e-06],
              [4.66712881e-05, 1.52227567e-05, 4.89958938e-06],
              [4.81022325e-05, 1.52632801e-05, 4.34061121e-06],
              [4.82442083e-05, 2.59471744e-05, 4.41810153e-06],
              [4.77407229e-05, 2.84563885e-05, 5.11112830e-06],
              [4.76316200e-05, 2.91304204e-05, 5.96908694e-06],
              [4.79773953e-05, 2.88553783e-05, 8.46229523e-06],
              [4.80109811e-05, 2.87334278e-05, 1.05174622e-05],
              [4.71629573e-05, 2.89586733e-05, 1.18731123e-05]]], dtype=float32)

    • Rig
      (depth, hour, tau_bins)
      float32
      13.2 4.297 3.056 ... 0.2232 0.2512

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      array([[[13.197857  ,  4.2973194 ,  3.0555031 ],
              [13.821407  ,  4.232544  ,  2.5504222 ],
              [13.510264  ,  4.2386236 ,  2.4945803 ],
              [13.20141   ,  4.2471313 ,  2.4653025 ],
              [13.149447  ,  4.319394  ,  2.4220955 ],
              [13.85933   ,  4.4229817 ,  2.3923457 ],
              [14.107593  ,  4.5487447 ,  2.3519185 ],
              [13.647481  ,  4.598955  ,  2.3381877 ],
              [13.462015  ,  4.5880175 ,  2.3576286 ],
              [13.113719  ,  4.6214905 ,  2.182707  ],
              [13.353025  ,  4.4489136 ,  2.2182493 ],
              [13.087497  ,  4.5611644 ,  2.2721744 ],
              [12.944699  ,  4.5314035 ,  2.3206887 ],
              [12.845064  ,  4.613967  ,  2.453578  ],
              [13.476563  ,  4.813492  ,  2.4708343 ],
              [13.658272  ,  5.07368   ,  2.7000542 ],
              [14.541157  ,  5.212734  ,  2.7889817 ],
              [14.787399  ,  5.2871675 ,  2.8942657 ],
              [15.181756  ,  5.5781765 ,  2.8768818 ],
              [15.022156  ,  5.3412776 ,  2.904497  ],
      ...
              [ 0.2605168 ,  0.24626791,  0.2186516 ],
              [ 0.26234898,  0.24100047,  0.27151155],
              [ 0.26460433,  0.22231369,  0.22628295],
              [ 0.26444855,  0.21137132,  0.32527298],
              [ 0.26379338,  0.2065137 ,  0.37389553],
              [ 0.26258442,  0.20844041,  0.3997121 ],
              [ 0.25847906,  0.22196066,  0.4238111 ],
              [ 0.25546002,  0.2310276 ,  0.44557902],
              [ 0.24929616,  0.24503568,  0.47095898],
              [ 0.25088966,  0.25116956,  0.47218096],
              [ 0.25009263,  0.25732547,  0.46698648],
              [ 0.24756661,  0.26599523,  0.46304277],
              [ 0.22898337,  0.22704817,  0.5012692 ],
              [ 0.21460304,  0.16282685,  0.3467214 ],
              [ 0.22194551,  0.16865185,  0.23832142],
              [ 0.223914  ,  0.17884398,  0.23131603],
              [ 0.22466302,  0.19137523,  0.21537249],
              [ 0.22704174,  0.20382346,  0.23264292],
              [ 0.23163387,  0.21370158,  0.24267618],
              [ 0.2359288 ,  0.22320737,  0.25116214]]], dtype=float32)

    • Rig_T
      (depth, hour, tau_bins)
      float32
      13.39 4.173 3.016 ... 0.1836 0.2279

      long_name :

      $Ri^g_T$

      array([[[13.39003   ,  4.1733675 ,  3.0161352 ],
              [14.119032  ,  4.050316  ,  2.7403107 ],
              [13.708002  ,  4.044218  ,  2.772739  ],
              [13.35049   ,  4.09664   ,  2.7337017 ],
              [13.440435  ,  4.2385445 ,  2.5692105 ],
              [13.888355  ,  4.367624  ,  2.4463243 ],
              [14.08926   ,  4.548192  ,  2.2969518 ],
              [13.814713  ,  4.5322876 ,  2.2213016 ],
              [13.61069   ,  4.5070376 ,  2.2090302 ],
              [13.551187  ,  4.5452027 ,  2.0633187 ],
              [13.787437  ,  4.4257965 ,  2.0763931 ],
              [13.39185   ,  4.480337  ,  2.1504233 ],
              [13.147581  ,  4.426073  ,  2.1903646 ],
              [13.178958  ,  4.545905  ,  2.2907367 ],
              [13.68602   ,  4.709573  ,  2.301371  ],
              [13.814043  ,  4.880429  ,  2.4320097 ],
              [14.562265  ,  5.046787  ,  2.570214  ],
              [14.989765  ,  5.1713166 ,  2.7265682 ],
              [15.491265  ,  5.3896675 ,  2.7282755 ],
              [15.379088  ,  5.16234   ,  2.7647042 ],
      ...
              [ 0.19949941,  0.19074827,  0.17702514],
              [ 0.20222469,  0.18197736,  0.18649122],
              [ 0.20144732,  0.16863236,  0.17766082],
              [ 0.20056407,  0.15703726,  0.18814558],
              [ 0.20053883,  0.14603414,  0.20615524],
              [ 0.1938774 ,  0.14094096,  0.23102301],
              [ 0.18646285,  0.14410391,  0.25327593],
              [ 0.1793328 ,  0.14658612,  0.28199226],
              [ 0.16956541,  0.1529384 ,  0.29382858],
              [ 0.16857266,  0.15509175,  0.3094122 ],
              [ 0.16314791,  0.15785547,  0.30625468],
              [ 0.16122043,  0.16049628,  0.3079362 ],
              [ 0.15586448,  0.14820942,  0.3269163 ],
              [ 0.15939882,  0.13319623,  0.2494062 ],
              [ 0.16583118,  0.13827197,  0.19804096],
              [ 0.1705277 ,  0.1412782 ,  0.19478822],
              [ 0.17406169,  0.15591256,  0.21430875],
              [ 0.17862558,  0.16688593,  0.21968636],
              [ 0.1818856 ,  0.17532237,  0.22169115],
              [ 0.18481219,  0.18359019,  0.22792467]]], dtype=float32)

    • tau
      (hour, tau_bins)
      float32
      0.02931 0.05352 ... 0.0542 0.08266

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([[0.02931377, 0.05352184, 0.08240295],
             [0.02966809, 0.05379798, 0.08218867],
             [0.02963378, 0.0531844 , 0.08235388],
             [0.02950549, 0.05342022, 0.08332323],
             [0.02959662, 0.05367155, 0.08316955],
             [0.0294683 , 0.05404468, 0.08309559],
             [0.02944375, 0.05421706, 0.08337168],
             [0.02952339, 0.05432522, 0.08384206],
             [0.02939557, 0.05427076, 0.08357246],
             [0.02919702, 0.05400485, 0.08424108],
             [0.02917114, 0.05389731, 0.08418184],
             [0.02895542, 0.05386308, 0.08414546],
             [0.02882244, 0.05380915, 0.0840569 ],
             [0.02902017, 0.05480089, 0.08480587],
             [0.02929266, 0.05536883, 0.08480069],
             [0.02949213, 0.05573643, 0.08509558],
             [0.02968458, 0.05615301, 0.08533888],
             [0.02993085, 0.05675283, 0.08584618],
             [0.02995646, 0.05718726, 0.08638418],
             [0.02968866, 0.05656635, 0.08475825],
             [0.0296479 , 0.05585978, 0.08439424],
             [0.02981201, 0.05550362, 0.08500274],
             [0.02963592, 0.0546338 , 0.08361351],
             [0.02963188, 0.05420088, 0.08266187]], dtype=float32)

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:   (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth     (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      xh        float64 -140.0
      yh        float64 0.0625
      yq        float64 -0.0625
    * hour      (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23
    * tau_bins  (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT        (depth, hour, tau_bins) float32 1.001e-06 0.000262 ... 0.0003525
      eps       (depth, hour, tau_bins) float32 4.898e-08 2.109e-07 ... 3.369e-08
      chi       (depth, hour, tau_bins) float32 6.284e-08 2.881e-07 ... 6.942e-09
      Jb        (depth, hour, tau_bins) float32 5.201e-10 9.148e-09 ... 8.505e-10
      Jq        (depth, hour, tau_bins) float64 -0.8128 -35.11 ... -31.0 -4.416
      S2        (depth, hour, tau_bins) float32 0.0002157 0.0002126 ... 2.707e-05
      N2        (depth, hour, tau_bins) float32 0.0002498 0.0001501 ... 1.074e-05
      Rig       (depth, hour, tau_bins) float32 0.6921 0.535 ... 0.4047 0.4431
      Rig_T     (depth, hour, tau_bins) float32 0.6525 0.4943 ... 0.3079 0.3627
      tau       (hour, tau_bins) float32 0.02913 0.05397 ... 0.05443 0.08317
  Attributes:
      title:    KD=0, KV=0

  new_baseline.hb

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-89., -69., -59., -49., -39., -29.])

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float32
      1.001e-06 0.000262 ... 0.0003525

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[1.00062653e-06, 2.62037240e-04, 1.05890748e-03],
              [1.00062664e-06, 2.72701523e-04, 1.09908008e-03],
              [1.00062664e-06, 2.90863565e-04, 1.02824823e-03],
              [1.00062664e-06, 2.96896702e-04, 1.00071402e-03],
              [1.00062664e-06, 2.66355200e-04, 1.02508185e-03],
              [1.00062653e-06, 2.53402599e-04, 9.46604297e-04],
              [1.00062653e-06, 2.26842472e-04, 8.61362787e-04],
              [1.00062653e-06, 2.20032278e-04, 8.83506727e-04],
              [1.00062664e-06, 2.11917271e-04, 8.30798468e-04],
              [1.00062664e-06, 2.03725998e-04, 8.30619712e-04],
              [1.00062675e-06, 1.92588341e-04, 8.49988079e-04],
              [1.00062675e-06, 1.90688588e-04, 8.54582933e-04],
              [1.00062675e-06, 1.91088926e-04, 7.87007099e-04],
              [1.00062664e-06, 1.57940449e-04, 7.20337499e-04],
              [1.00062653e-06, 1.38884861e-04, 6.53320050e-04],
              [1.00062653e-06, 1.24576676e-04, 6.12310017e-04],
              [1.00062653e-06, 1.00052304e-04, 7.07846077e-04],
              [1.00062641e-06, 9.21100873e-05, 7.37336930e-04],
              [1.00062641e-06, 8.17428736e-05, 7.50365201e-04],
              [1.00062641e-06, 1.24435348e-04, 8.18357163e-04],
      ...
              [1.41367852e-03, 1.79877831e-03, 1.71377081e-02],
              [1.37466111e-03, 2.30275956e-03, 3.32856625e-02],
              [1.34840910e-03, 3.03058885e-03, 4.47302125e-02],
              [1.38495094e-03, 4.78022080e-03, 5.33157960e-02],
              [1.47192599e-03, 9.51274391e-03, 5.98784201e-02],
              [1.55525003e-03, 1.41898468e-02, 6.55668080e-02],
              [1.60700711e-03, 1.95938740e-02, 7.16438368e-02],
              [1.77496276e-03, 2.32385416e-02, 7.43482858e-02],
              [1.91565300e-03, 2.69467719e-02, 7.60267600e-02],
              [1.93555490e-03, 2.79643107e-02, 7.59711117e-02],
              [2.08756095e-03, 2.94796471e-02, 7.63885081e-02],
              [2.20756372e-03, 3.05341445e-02, 7.48512000e-02],
              [2.07593641e-03, 3.59162269e-03, 2.28384528e-02],
              [2.05276557e-03, 1.21652649e-03, 2.78259465e-03],
              [2.36642803e-03, 1.12493162e-03, 2.53933412e-03],
              [2.31716968e-03, 1.69391045e-03, 1.25961495e-03],
              [2.22260458e-03, 1.90275419e-03, 6.10022515e-04],
              [2.06597475e-03, 1.90889882e-03, 6.11530559e-04],
              [1.90941000e-03, 1.74662611e-03, 4.70483530e-04],
              [1.74895767e-03, 1.62311806e-03, 3.52516159e-04]]], dtype=float32)

    • eps
      (depth, hour, tau_bins)
      float32
      4.898e-08 2.109e-07 ... 3.369e-08

      long_name :

      $ε$

      units :

      W/kg

      array([[[4.89753944e-08, 2.10869956e-07, 3.65941844e-07],
              [5.13541139e-08, 2.15127756e-07, 3.64754300e-07],
              [5.41888241e-08, 2.15065000e-07, 3.46860901e-07],
              [5.61319773e-08, 2.09817756e-07, 3.30694377e-07],
              [5.58599709e-08, 2.04127033e-07, 3.10800772e-07],
              [5.33404716e-08, 1.98032964e-07, 2.99553619e-07],
              [5.17132221e-08, 1.91185251e-07, 2.81550996e-07],
              [5.10814289e-08, 1.86589659e-07, 2.75198403e-07],
              [5.25533004e-08, 1.82796654e-07, 2.59321155e-07],
              [5.27817008e-08, 1.74193303e-07, 2.56438966e-07],
              [5.35027027e-08, 1.69497440e-07, 2.46893592e-07],
              [5.34857207e-08, 1.68255823e-07, 2.44179517e-07],
              [5.52743167e-08, 1.66044600e-07, 2.39957984e-07],
              [5.02844699e-08, 1.62360294e-07, 2.42779464e-07],
              [4.44484414e-08, 1.55758229e-07, 2.45422200e-07],
              [4.25727400e-08, 1.50236517e-07, 2.38815062e-07],
              [3.93804953e-08, 1.47907002e-07, 2.66488684e-07],
              [3.63328496e-08, 1.53234055e-07, 2.91657159e-07],
              [3.25132916e-08, 1.56820207e-07, 3.12330428e-07],
              [3.14434097e-08, 1.66755285e-07, 3.35571485e-07],
      ...
              [1.32510735e-07, 2.67840505e-07, 6.84367080e-07],
              [1.40816013e-07, 4.82158498e-07, 8.39150061e-07],
              [1.43348117e-07, 6.31026808e-07, 7.57897965e-07],
              [1.52440421e-07, 7.13276108e-07, 6.29790861e-07],
              [1.71569596e-07, 6.93433947e-07, 5.20004505e-07],
              [1.98224043e-07, 6.28771090e-07, 4.44837781e-07],
              [2.26603930e-07, 5.59493174e-07, 3.89336378e-07],
              [2.64661935e-07, 4.87133264e-07, 3.41043062e-07],
              [2.79234030e-07, 4.23746258e-07, 3.01816016e-07],
              [2.74314488e-07, 3.87589012e-07, 2.78628193e-07],
              [2.67226142e-07, 3.57912597e-07, 2.75003202e-07],
              [2.66750249e-07, 3.30479708e-07, 2.65963621e-07],
              [2.28383229e-07, 1.10453826e-07, 1.06349248e-07],
              [2.19336044e-07, 7.44121991e-08, 2.20961454e-08],
              [2.48404348e-07, 1.06142551e-07, 2.67914810e-08],
              [2.43364752e-07, 1.25137674e-07, 2.28767245e-08],
              [2.34714150e-07, 1.32180332e-07, 2.11699458e-08],
              [2.13275101e-07, 1.27884704e-07, 2.26537828e-08],
              [1.95051314e-07, 1.19670261e-07, 2.58323478e-08],
              [1.77437926e-07, 1.09955913e-07, 3.36903945e-08]]], dtype=float32)

    • chi
      (depth, hour, tau_bins)
      float32
      6.284e-08 2.881e-07 ... 6.942e-09

      long_name :

      $χ$

      units :

      C^2/s

      array([[[6.28427301e-08, 2.88068264e-07, 8.26305950e-07],
              [6.27368166e-08, 2.98960629e-07, 8.71046439e-07],
              [6.44347011e-08, 3.12472736e-07, 9.56297868e-07],
              [6.58234427e-08, 2.90687467e-07, 8.94805169e-07],
              [6.38682138e-08, 2.69036633e-07, 8.48512173e-07],
              [6.23498408e-08, 2.60920700e-07, 7.95632900e-07],
              [6.17878939e-08, 2.35277298e-07, 7.43213093e-07],
              [6.09875315e-08, 2.30552388e-07, 7.15444685e-07],
              [6.19845935e-08, 2.33280574e-07, 6.38697657e-07],
              [6.30164294e-08, 2.23609277e-07, 6.13384714e-07],
              [6.33949782e-08, 2.18304905e-07, 6.51640221e-07],
              [6.44858815e-08, 2.16931255e-07, 6.27009797e-07],
              [6.66231301e-08, 2.17100066e-07, 5.76267610e-07],
              [6.13616891e-08, 2.07990112e-07, 5.76540401e-07],
              [5.89936313e-08, 1.95030083e-07, 5.98523343e-07],
              [5.78152637e-08, 1.80715574e-07, 5.96839300e-07],
              [5.59458790e-08, 1.73955854e-07, 6.28465671e-07],
              [5.44911316e-08, 1.69204824e-07, 6.99216798e-07],
              [5.31692095e-08, 1.68147437e-07, 7.60493265e-07],
              [5.20742489e-08, 1.85152899e-07, 7.71945395e-07],
      ...
              [8.01801932e-08, 1.43546359e-07, 3.93581416e-07],
              [8.15358305e-08, 2.63959834e-07, 4.96796190e-07],
              [8.34458191e-08, 3.63753145e-07, 3.89451685e-07],
              [9.06807145e-08, 3.98819338e-07, 2.63717595e-07],
              [9.65297176e-08, 3.69848379e-07, 1.95779862e-07],
              [1.16798915e-07, 3.11457910e-07, 1.47851480e-07],
              [1.31431392e-07, 2.54156788e-07, 1.17898125e-07],
              [1.45972948e-07, 2.09105082e-07, 9.08319109e-08],
              [1.43596168e-07, 1.64574814e-07, 7.50877760e-08],
              [1.30332225e-07, 1.36982720e-07, 7.11929431e-08],
              [1.25370548e-07, 1.19903476e-07, 6.65046258e-08],
              [1.23980243e-07, 1.06661567e-07, 6.42934452e-08],
              [1.02193404e-07, 1.57689470e-08, 2.12460556e-08],
              [1.21662225e-07, 7.79856713e-09, 2.85935986e-09],
              [1.81704664e-07, 1.52409090e-08, 4.98988229e-09],
              [1.96759984e-07, 4.02906437e-08, 3.78793130e-09],
              [1.93130234e-07, 6.06322743e-08, 3.38508843e-09],
              [1.76792256e-07, 6.97176006e-08, 4.78815654e-09],
              [1.61031181e-07, 6.62385702e-08, 5.76574166e-09],
              [1.42380628e-07, 6.12754292e-08, 6.94171742e-09]]], dtype=float32)

    • Jb
      (depth, hour, tau_bins)
      float32
      5.201e-10 9.148e-09 ... 8.505e-10

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[5.20121723e-10, 9.14844023e-09, 4.93047665e-08],
              [5.22739962e-10, 9.68542224e-09, 5.20738865e-08],
              [5.26788724e-10, 1.03196767e-08, 5.22399120e-08],
              [5.31486521e-10, 9.74224612e-09, 5.16224858e-08],
              [5.28820432e-10, 8.79323636e-09, 4.80655693e-08],
              [5.22581700e-10, 7.71664954e-09, 4.50965629e-08],
              [5.21203691e-10, 5.64962477e-09, 4.01015399e-08],
              [5.18142307e-10, 5.64664893e-09, 3.91272224e-08],
              [5.23529831e-10, 5.48414247e-09, 3.74587685e-08],
              [5.28541544e-10, 5.04714226e-09, 3.46567788e-08],
              [5.26243826e-10, 4.30064651e-09, 3.66826640e-08],
              [5.30970545e-10, 4.58714178e-09, 3.40342439e-08],
              [5.40062717e-10, 4.76399675e-09, 3.22511404e-08],
              [5.22477839e-10, 3.94990529e-09, 3.11737196e-08],
              [5.14006226e-10, 2.94821079e-09, 3.12277812e-08],
              [5.08801501e-10, 2.20570762e-09, 3.06338883e-08],
              [5.04617959e-10, 1.54705970e-09, 3.28621717e-08],
              [4.98949326e-10, 1.16123022e-09, 3.47503857e-08],
              [4.88723784e-10, 1.06063713e-09, 3.58807526e-08],
              [4.87477392e-10, 2.45552156e-09, 3.57378731e-08],
      ...
              [1.19724994e-08, 1.70177330e-08, 1.26920753e-07],
              [1.19211130e-08, 2.91519573e-08, 1.85755169e-07],
              [1.16928520e-08, 4.86725327e-08, 1.75252964e-07],
              [1.28964057e-08, 7.18805637e-08, 1.54877227e-07],
              [1.38640353e-08, 8.52027426e-08, 1.34078221e-07],
              [1.57190652e-08, 8.47513775e-08, 1.16542580e-07],
              [1.61323648e-08, 8.39835579e-08, 1.00404989e-07],
              [1.95944843e-08, 7.88693768e-08, 8.40583709e-08],
              [1.87968698e-08, 7.23438234e-08, 7.34735437e-08],
              [1.98578043e-08, 6.28177261e-08, 6.60879067e-08],
              [1.82452986e-08, 6.05719066e-08, 6.59063346e-08],
              [2.02872936e-08, 5.50794574e-08, 6.34640145e-08],
              [1.40410705e-08, 6.71363454e-09, 1.99716030e-08],
              [1.54548694e-08, 1.65583369e-09, 1.90645566e-09],
              [2.33801369e-08, 1.92635197e-09, 2.99118907e-09],
              [2.59499373e-08, 5.00197261e-09, 1.58494307e-09],
              [2.67266209e-08, 8.41553582e-09, 7.80392917e-10],
              [2.56543515e-08, 9.63710711e-09, 8.30871316e-10],
              [2.25431442e-08, 9.36158528e-09, 7.23190507e-10],
              [2.03528803e-08, 8.93805474e-09, 8.50497450e-10]]], dtype=float32)

    • Jq
      (depth, hour, tau_bins)
      float64
      -0.8128 -35.11 ... -31.0 -4.416

      units :

      W/m^2

      long_name :

      $J_q^t$

      array([[[  -0.81275251,  -35.10540508, -107.92516258],
              [  -0.81452745,  -37.30334659, -111.43608918],
              [  -0.81740106,  -38.24310327, -110.81864896],
              [  -0.8337643 ,  -36.90952601, -106.07761669],
              [  -0.83078597,  -33.39633727, -102.65993507],
              [  -0.81278874,  -31.48373516,  -96.20815795],
              [  -0.80897951,  -29.27580381,  -91.31173044],
              [  -0.80735026,  -28.07736327,  -86.57831432],
              [  -0.81581746,  -28.11210656,  -80.24805898],
              [  -0.8243209 ,  -26.31316743,  -79.05538436],
              [  -0.82850863,  -24.78306735,  -78.83334196],
              [  -0.83782546,  -24.51323225,  -76.66792171],
              [  -0.84537657,  -25.62885892,  -73.43640519],
              [  -0.81986974,  -22.42427483,  -71.26076615],
              [  -0.79646231,  -19.5188711 ,  -69.76079995],
              [  -0.78984496,  -16.53241175,  -67.13581603],
              [  -0.77231664,  -13.41119337,  -74.19449757],
              [  -0.7643023 ,  -12.92565907,  -79.32650121],
              [  -0.75232554,  -11.57467935,  -82.422927  ],
              [  -0.7441352 ,  -17.67938361,  -86.26225594],
      ...
              [ -31.87862027,  -44.16697882, -261.96079925],
              [ -31.56887868,  -72.83843194, -377.9896837 ],
              [ -30.9545401 , -128.39360995, -375.99278148],
              [ -31.56165977, -188.84136826, -353.71039164],
              [ -35.07868203, -213.64488296, -324.67284683],
              [ -38.16875777, -216.86511225, -295.0096038 ],
              [ -40.91438287, -214.01366732, -270.30288383],
              [ -47.57095843, -204.76566038, -247.3864047 ],
              [ -51.89456633, -194.27813913, -231.64378862],
              [ -53.58953487, -177.50533146, -221.23058939],
              [ -53.27081906, -171.74451817, -210.98684748],
              [ -57.0413013 , -164.76087878, -205.40908003],
              [ -40.80732238,  -27.55403688,  -71.54460267],
              [ -44.89828358,   -8.20514559,   -9.65387598],
              [ -59.21442771,  -10.90296723,  -11.91643733],
              [ -59.91662008,  -24.30769411,   -7.67025163],
              [ -60.46739161,  -32.15060793,   -4.7937284 ],
              [ -55.39888652,  -34.30766255,   -5.44876747],
              [ -51.89013205,  -32.49015759,   -5.0439275 ],
              [ -46.88159532,  -31.00173399,   -4.41609556]]])

    • S2
      (depth, hour, tau_bins)
      float32
      0.0002157 0.0002126 ... 2.707e-05

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([[[2.15672277e-04, 2.12571860e-04, 2.09773949e-04],
              [2.11442632e-04, 2.13960826e-04, 2.08297279e-04],
              [2.12287967e-04, 2.13467691e-04, 2.08366328e-04],
              [2.10965911e-04, 2.11972176e-04, 2.07678633e-04],
              [2.12011786e-04, 2.13226915e-04, 2.05537624e-04],
              [2.10400962e-04, 2.13552674e-04, 2.04386102e-04],
              [2.12897561e-04, 2.12111685e-04, 2.02022580e-04],
              [2.10545331e-04, 2.11847306e-04, 1.97839865e-04],
              [2.11565115e-04, 2.10612125e-04, 1.97103727e-04],
              [2.16023400e-04, 2.07201680e-04, 1.95945642e-04],
              [2.16643792e-04, 2.06816840e-04, 1.92040781e-04],
              [2.15596141e-04, 2.06519107e-04, 1.87373429e-04],
              [2.16904009e-04, 2.03615797e-04, 1.89063925e-04],
              [2.13125197e-04, 2.04004653e-04, 1.98501541e-04],
              [2.10339669e-04, 2.04935292e-04, 1.98550842e-04],
              [2.10460654e-04, 2.03726391e-04, 2.05102784e-04],
              [2.10142141e-04, 2.03563191e-04, 2.09907332e-04],
              [2.13177133e-04, 2.04036303e-04, 2.12833940e-04],
              [2.09707534e-04, 2.06934783e-04, 2.16156070e-04],
              [2.09469115e-04, 2.09337260e-04, 2.18377172e-04],
      ...
              [7.56139125e-05, 8.71341690e-05, 4.80451272e-05],
              [7.91544371e-05, 1.01554950e-04, 2.81878711e-05],
              [8.30380959e-05, 1.09442073e-04, 1.88173381e-05],
              [8.69750729e-05, 1.01881058e-04, 1.30142435e-05],
              [9.41247126e-05, 7.86011806e-05, 9.69898065e-06],
              [9.96056042e-05, 5.49317556e-05, 7.44869567e-06],
              [1.06645661e-04, 3.67982102e-05, 5.92534889e-06],
              [1.08007203e-04, 2.58306009e-05, 4.90106504e-06],
              [1.06873224e-04, 1.89379862e-05, 4.29951342e-06],
              [1.06376858e-04, 1.58985531e-05, 4.05997798e-06],
              [1.01732832e-04, 1.36065619e-05, 3.86104784e-06],
              [1.00730147e-04, 1.22374022e-05, 3.82177495e-06],
              [1.14109302e-04, 2.29600610e-05, 3.37873757e-06],
              [1.07913307e-04, 4.26053302e-05, 4.52991026e-06],
              [1.08090324e-04, 5.52821584e-05, 5.62377090e-06],
              [1.04775114e-04, 5.55655897e-05, 7.24206393e-06],
              [1.01420417e-04, 5.40483234e-05, 1.19902697e-05],
              [9.72153139e-05, 5.22391856e-05, 1.74126035e-05],
              [9.16627687e-05, 5.07932564e-05, 2.12584673e-05],
              [8.66877963e-05, 5.03534393e-05, 2.70670953e-05]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float32
      0.0002498 0.0001501 ... 1.074e-05

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      array([[[2.49770761e-04, 1.50098756e-04, 1.11120258e-04],
              [2.47220974e-04, 1.48095016e-04, 1.09469358e-04],
              [2.45606410e-04, 1.47212602e-04, 1.16273659e-04],
              [2.44910567e-04, 1.46733626e-04, 1.18762495e-04],
              [2.46512733e-04, 1.49197745e-04, 1.12651309e-04],
              [2.48484663e-04, 1.50325548e-04, 1.12898553e-04],
              [2.50331883e-04, 1.52102992e-04, 1.12781214e-04],
              [2.47574528e-04, 1.54107634e-04, 1.07344546e-04],
              [2.44933646e-04, 1.54900685e-04, 1.09973000e-04],
              [2.42529481e-04, 1.55066184e-04, 1.11506415e-04],
              [2.43119634e-04, 1.55268455e-04, 1.09090055e-04],
              [2.40484907e-04, 1.56507798e-04, 1.07217173e-04],
              [2.42932118e-04, 1.54143461e-04, 1.09352390e-04],
              [2.47160351e-04, 1.55096990e-04, 1.11676884e-04],
              [2.48186872e-04, 1.56149719e-04, 1.17096948e-04],
              [2.47517542e-04, 1.57528339e-04, 1.21044686e-04],
              [2.51487305e-04, 1.58637442e-04, 1.20155579e-04],
              [2.54361890e-04, 1.61505086e-04, 1.20364450e-04],
              [2.58087355e-04, 1.62304452e-04, 1.22957426e-04],
              [2.59101274e-04, 1.60314288e-04, 1.22088502e-04],
      ...
              [2.85044662e-05, 2.38834564e-05, 1.88644735e-05],
              [2.91151373e-05, 2.64380724e-05, 1.56137939e-05],
              [2.97974948e-05, 2.77846120e-05, 1.16638857e-05],
              [3.07708033e-05, 2.75006969e-05, 8.30140198e-06],
              [3.24869034e-05, 2.40745630e-05, 6.47047591e-06],
              [3.33005883e-05, 1.96775745e-05, 5.33579077e-06],
              [3.45363878e-05, 1.50565565e-05, 4.39064070e-06],
              [3.53112300e-05, 1.14167287e-05, 3.83091219e-06],
              [3.45137305e-05, 8.98336566e-06, 3.45510307e-06],
              [3.41937484e-05, 7.86413875e-06, 3.39021835e-06],
              [3.30625990e-05, 7.16474597e-06, 3.19372793e-06],
              [3.16721853e-05, 6.60015530e-06, 3.07497498e-06],
              [3.25626825e-05, 6.08912433e-06, 2.91151355e-06],
              [3.47301902e-05, 7.46536080e-06, 2.89518221e-06],
              [3.47736095e-05, 1.20941550e-05, 3.61292769e-06],
              [3.44612490e-05, 1.47121618e-05, 4.20345714e-06],
              [3.39114413e-05, 1.61574008e-05, 5.35977597e-06],
              [3.25952788e-05, 1.72388645e-05, 7.28179202e-06],
              [3.15559228e-05, 1.78157061e-05, 8.98511098e-06],
              [3.07502341e-05, 1.84755536e-05, 1.07377618e-05]]], dtype=float32)

    • Rig
      (depth, hour, tau_bins)
      float32
      0.6921 0.535 ... 0.4047 0.4431

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      array([[[0.6920738 , 0.5349543 , 0.4580132 ],
              [0.6879399 , 0.5314694 , 0.4592346 ],
              [0.6806804 , 0.5308178 , 0.4657764 ],
              [0.6791985 , 0.53030324, 0.47593078],
              [0.6806549 , 0.5339809 , 0.4767241 ],
              [0.68695664, 0.53564453, 0.48169115],
              [0.6896516 , 0.53775823, 0.49365795],
              [0.6888744 , 0.5401193 , 0.49436718],
              [0.68251336, 0.5424917 , 0.5011655 ],
              [0.68021417, 0.54622084, 0.5020577 ],
              [0.6766766 , 0.5488351 , 0.5055933 ],
              [0.6775784 , 0.55120665, 0.5052763 ],
              [0.6728198 , 0.55457515, 0.5086805 ],
              [0.6839738 , 0.55721325, 0.51228535],
              [0.691461  , 0.5597189 , 0.5188104 ],
              [0.69568074, 0.56400734, 0.52135473],
              [0.7045409 , 0.56998813, 0.5013685 ],
              [0.7121001 , 0.56713057, 0.49566406],
              [0.72767735, 0.56552804, 0.490233  ],
              [0.7288958 , 0.5595642 , 0.47649854],
      ...
              [0.40634552, 0.33188626, 0.5003445 ],
              [0.39810854, 0.3208425 , 0.581831  ],
              [0.39363727, 0.32485807, 0.6355092 ],
              [0.38015234, 0.34808362, 0.66806245],
              [0.36744684, 0.39145422, 0.6928216 ],
              [0.35197896, 0.42392755, 0.7202125 ],
              [0.34439027, 0.46392506, 0.74636894],
              [0.34399074, 0.4974593 , 0.77452666],
              [0.3449704 , 0.5331218 , 0.7831469 ],
              [0.34179208, 0.54510725, 0.80061936],
              [0.34201652, 0.5604679 , 0.8105681 ],
              [0.34889582, 0.5762068 , 0.81085813],
              [0.2864733 , 0.38954324, 0.84244406],
              [0.31387162, 0.28162244, 0.6502222 ],
              [0.32518497, 0.30333692, 0.62802637],
              [0.3409173 , 0.334327  , 0.56850284],
              [0.3572793 , 0.35580266, 0.46713182],
              [0.36969233, 0.37518197, 0.453887  ],
              [0.37960896, 0.39090776, 0.44790077],
              [0.39049977, 0.40465185, 0.44312316]]], dtype=float32)

    • Rig_T
      (depth, hour, tau_bins)
      float32
      0.6525 0.4943 ... 0.3079 0.3627

      long_name :

      $Ri^g_T$

      array([[[0.6524795 , 0.49431792, 0.40401042],
              [0.6476281 , 0.49186328, 0.40452516],
              [0.6441446 , 0.4895203 , 0.4143563 ],
              [0.64207554, 0.49051276, 0.41974872],
              [0.6392752 , 0.49562863, 0.4259835 ],
              [0.6485602 , 0.49845082, 0.43141162],
              [0.65098137, 0.50073636, 0.43876797],
              [0.6474688 , 0.50299937, 0.4344629 ],
              [0.64145947, 0.50219667, 0.44302076],
              [0.63325673, 0.5045558 , 0.44308442],
              [0.6337309 , 0.50653815, 0.44803062],
              [0.63019466, 0.5095291 , 0.45472556],
              [0.630605  , 0.50949144, 0.4569693 ],
              [0.6359448 , 0.51431185, 0.46192995],
              [0.64430714, 0.5192264 , 0.472704  ],
              [0.65253395, 0.52388585, 0.47870392],
              [0.6600066 , 0.52851456, 0.4612955 ],
              [0.6755862 , 0.5250453 , 0.45920655],
              [0.68343335, 0.52443343, 0.45433536],
              [0.6843606 , 0.52000076, 0.43686157],
      ...
              [0.29926416, 0.23857142, 0.26230258],
              [0.2974115 , 0.21804348, 0.30031466],
              [0.28810856, 0.21146052, 0.32518813],
              [0.28323996, 0.21127589, 0.3577328 ],
              [0.26677144, 0.2186438 , 0.38158107],
              [0.2535754 , 0.23513977, 0.39806974],
              [0.24205409, 0.25546497, 0.40978163],
              [0.2332109 , 0.2754921 , 0.42806375],
              [0.22958612, 0.2987565 , 0.43636626],
              [0.23360637, 0.306973  , 0.4462604 ],
              [0.23348331, 0.31307265, 0.45968074],
              [0.23676656, 0.32316506, 0.46267766],
              [0.22447279, 0.25387365, 0.5430862 ],
              [0.24416187, 0.2449134 , 0.4584749 ],
              [0.2532297 , 0.24663666, 0.46583867],
              [0.26291543, 0.25914267, 0.46689865],
              [0.27117586, 0.27355063, 0.43329036],
              [0.28200358, 0.28652632, 0.39690372],
              [0.28587258, 0.29844803, 0.38097277],
              [0.29391518, 0.30785322, 0.36271706]]], dtype=float32)

    • tau
      (hour, tau_bins)
      float32
      0.02913 0.05397 ... 0.05443 0.08317

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([[0.02912814, 0.05397057, 0.08243489],
             [0.02951978, 0.05395206, 0.08258021],
             [0.02955842, 0.0539168 , 0.08289316],
             [0.02935282, 0.05389314, 0.08354414],
             [0.02921309, 0.05400709, 0.083749  ],
             [0.02929978, 0.05425711, 0.0844179 ],
             [0.02939584, 0.05473844, 0.08448129],
             [0.02932024, 0.05432779, 0.08426537],
             [0.02936672, 0.05443417, 0.08411882],
             [0.02933902, 0.05419946, 0.08512807],
             [0.0292527 , 0.05430375, 0.08504955],
             [0.02921882, 0.05419347, 0.08442991],
             [0.02883983, 0.05426022, 0.08442935],
             [0.02913588, 0.05487758, 0.08464634],
             [0.02929619, 0.05555559, 0.08535865],
             [0.02973468, 0.0561522 , 0.08564808],
             [0.02978581, 0.05643295, 0.08602639],
             [0.0299926 , 0.05669548, 0.08611111],
             [0.0300919 , 0.05693631, 0.08645762],
             [0.02996605, 0.05639978, 0.08547585],
             [0.02971533, 0.05582306, 0.08512087],
             [0.02977535, 0.05557946, 0.08559605],
             [0.0296651 , 0.05494839, 0.08416149],
             [0.02947801, 0.05443047, 0.08317043]], dtype=float32)

  • Attributes: (1)

    title :

    KD=0, KV=0

  <xarray.DatasetView>
  Dimensions:   (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth     (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      xh        float64 -140.0
      yh        float64 0.0625
      yq        float64 -0.0625
    * hour      (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23
    * tau_bins  (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT        (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 0.0001793
      eps       (depth, hour, tau_bins) float32 6.182e-10 1.664e-09 ... 1.489e-08
      chi       (depth, hour, tau_bins) float32 1.739e-08 2.265e-08 ... 2.921e-09
      Jb        (depth, hour, tau_bins) float32 2.555e-10 2.914e-10 ... 2.291e-11
      Jq        (depth, hour, tau_bins) float64 -0.4038 -0.4617 ... -30.73 -1.899
      S2        (depth, hour, tau_bins) float32 6.214e-05 0.0002147 ... 3.608e-05
      N2        (depth, hour, tau_bins) float32 0.0002396 0.0002716 ... 1.035e-05
      Rig       (depth, hour, tau_bins) float32 3.263 1.133 ... 0.2238 0.2969
      Rig_T     (depth, hour, tau_bins) float32 3.261 1.081 ... 0.1893 0.283
      tau       (hour, tau_bins) float32 0.02975 0.05414 ... 0.05437 0.08353
  Attributes:
      title:    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  new_baseline.kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-89., -69., -59., -49., -39., -29.])

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float32
      1.001e-06 1.001e-06 ... 0.0001793

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006263e-06, 1.0006262e-06],
              [1.0006263e-06, 1.0006262e-06, 1.0006262e-06],
      ...
              [9.4132446e-04, 1.0125230e-03, 3.3146515e-03],
              [9.1822050e-04, 1.1766625e-03, 1.4005989e-02],
              [9.2203892e-04, 1.2403573e-03, 2.3555636e-02],
              [9.2468399e-04, 1.4708596e-03, 3.6420863e-02],
              [9.4251416e-04, 1.7338136e-03, 4.3797132e-02],
              [9.6162333e-04, 3.1932166e-03, 4.8042994e-02],
              [9.9941925e-04, 6.7671593e-03, 5.3763784e-02],
              [1.0284228e-03, 1.0356332e-02, 5.6218788e-02],
              [1.0461721e-03, 1.3733745e-02, 5.8365785e-02],
              [1.0795848e-03, 1.4616154e-02, 5.8618966e-02],
              [1.1059251e-03, 1.5877362e-02, 5.8043797e-02],
              [1.1142871e-03, 1.6551722e-02, 5.5786945e-02],
              [1.2179909e-03, 1.7418606e-03, 1.4232323e-02],
              [1.2699188e-03, 1.2363050e-03, 6.4157473e-04],
              [1.3006497e-03, 1.2935506e-03, 7.0208183e-04],
              [1.2927618e-03, 1.3108684e-03, 2.6574192e-04],
              [1.2749400e-03, 1.2415671e-03, 2.4541048e-04],
              [1.2317502e-03, 1.1815999e-03, 2.0568147e-04],
              [1.1754853e-03, 1.1246946e-03, 2.0106597e-04],
              [1.1240497e-03, 1.0682463e-03, 1.7925282e-04]]], dtype=float32)

    • eps
      (depth, hour, tau_bins)
      float32
      6.182e-10 1.664e-09 ... 1.489e-08

      long_name :

      $ε$

      units :

      W/kg

      array([[[6.18212148e-10, 1.66353620e-09, 6.16506490e-09],
              [6.21017682e-10, 1.67842806e-09, 9.27319999e-09],
              [6.21645957e-10, 1.73616943e-09, 1.19016654e-08],
              [6.23542662e-10, 1.70288716e-09, 1.31447342e-08],
              [6.13696038e-10, 1.66104730e-09, 1.81335498e-08],
              [5.88845916e-10, 1.63481761e-09, 1.95871586e-08],
              [5.78864401e-10, 1.61448899e-09, 1.34869200e-08],
              [5.73052494e-10, 1.55730606e-09, 2.45271714e-08],
              [5.76379722e-10, 1.56207058e-09, 2.89932629e-08],
              [5.83151361e-10, 1.56888014e-09, 2.43189096e-08],
              [5.94897742e-10, 1.52692259e-09, 2.98047134e-08],
              [5.96837024e-10, 1.54409485e-09, 2.83610735e-08],
              [6.04147454e-10, 1.57005697e-09, 2.05234958e-08],
              [5.86772519e-10, 1.54123359e-09, 1.26474884e-08],
              [5.83876281e-10, 1.46456780e-09, 1.18291696e-08],
              [5.70010428e-10, 1.43973800e-09, 7.06141190e-09],
              [5.60596181e-10, 1.38091871e-09, 7.66254438e-09],
              [5.49527090e-10, 1.35822509e-09, 8.44123882e-09],
              [5.54969071e-10, 1.33498079e-09, 6.55933352e-09],
              [5.56361068e-10, 1.39136047e-09, 1.06588587e-08],
      ...
              [1.69150908e-07, 2.43112993e-07, 9.39682195e-07],
              [1.69089219e-07, 2.83000759e-07, 1.08108452e-06],
              [1.69189576e-07, 4.53984995e-07, 1.09987320e-06],
              [1.76490246e-07, 6.83876010e-07, 1.02829711e-06],
              [1.87332532e-07, 8.34422167e-07, 8.87234876e-07],
              [2.03888462e-07, 8.63935895e-07, 7.73098748e-07],
              [2.31252272e-07, 8.49893922e-07, 6.49428159e-07],
              [2.59941800e-07, 7.81794597e-07, 5.79500636e-07],
              [2.96473843e-07, 7.32776357e-07, 5.20611252e-07],
              [3.03595698e-07, 6.56302234e-07, 4.67348343e-07],
              [3.09861889e-07, 6.14048531e-07, 4.71917417e-07],
              [3.30604593e-07, 5.71120836e-07, 4.46970546e-07],
              [2.76740167e-07, 2.10724153e-07, 1.51785173e-07],
              [2.83532700e-07, 1.67954241e-07, 2.14249205e-08],
              [2.78899847e-07, 1.95938682e-07, 3.27067085e-08],
              [2.78074339e-07, 2.04555377e-07, 2.03654213e-08],
              [2.68917859e-07, 1.91863649e-07, 1.66435079e-08],
              [2.55161297e-07, 1.72959460e-07, 1.53984452e-08],
              [2.40075252e-07, 1.58456729e-07, 1.62956582e-08],
              [2.23403930e-07, 1.48273898e-07, 1.48935024e-08]]], dtype=float32)

    • chi
      (depth, hour, tau_bins)
      float32
      1.739e-08 2.265e-08 ... 2.921e-09

      long_name :

      $χ$

      units :

      C^2/s

      array([[[1.73884356e-08, 2.26525483e-08, 2.44861411e-08],
              [1.74541945e-08, 2.25107541e-08, 2.62088093e-08],
              [1.74165553e-08, 2.30953781e-08, 2.58756945e-08],
              [1.76193122e-08, 2.29807107e-08, 2.62177746e-08],
              [1.75198185e-08, 2.28707417e-08, 2.60189026e-08],
              [1.77797759e-08, 2.27358683e-08, 2.63329873e-08],
              [1.77949939e-08, 2.27717489e-08, 2.54955133e-08],
              [1.77553900e-08, 2.28247217e-08, 2.53485677e-08],
              [1.76991506e-08, 2.27228103e-08, 2.53738861e-08],
              [1.76896275e-08, 2.26282602e-08, 2.46510261e-08],
              [1.72673751e-08, 2.28223289e-08, 2.49068179e-08],
              [1.70976175e-08, 2.28065726e-08, 2.59466173e-08],
              [1.74429111e-08, 2.25265229e-08, 2.53189221e-08],
              [1.71922050e-08, 2.24622845e-08, 2.49400109e-08],
              [1.72470962e-08, 2.21477912e-08, 2.48894487e-08],
              [1.71766423e-08, 2.19773977e-08, 2.52571652e-08],
              [1.71460073e-08, 2.18138965e-08, 2.48438727e-08],
              [1.68117325e-08, 2.15464286e-08, 2.56811177e-08],
              [1.70101799e-08, 2.12656666e-08, 2.53530850e-08],
              [1.67263465e-08, 2.17167706e-08, 2.57135699e-08],
      ...
              [1.75568431e-07, 1.69639179e-07, 3.88403151e-07],
              [1.75191545e-07, 1.87313177e-07, 5.02682212e-07],
              [1.78678505e-07, 2.76781094e-07, 4.66718802e-07],
              [1.79366026e-07, 3.55949112e-07, 4.29998522e-07],
              [1.93214674e-07, 4.01829709e-07, 3.54981921e-07],
              [2.06669668e-07, 3.73105195e-07, 2.72762577e-07],
              [2.30700508e-07, 3.66185759e-07, 2.02141905e-07],
              [2.37789180e-07, 3.12547570e-07, 1.59825987e-07],
              [2.32979346e-07, 2.58506304e-07, 1.35901701e-07],
              [2.31390715e-07, 2.30672811e-07, 1.19482976e-07],
              [2.24134709e-07, 2.02506129e-07, 1.22481609e-07],
              [2.03919456e-07, 1.81497555e-07, 1.22820964e-07],
              [2.27858976e-07, 2.54538524e-08, 2.69130194e-08],
              [2.34801988e-07, 3.19046833e-08, 1.72959358e-09],
              [2.60971547e-07, 7.96455524e-08, 3.60839358e-09],
              [2.78977524e-07, 9.71027134e-08, 2.21884200e-09],
              [2.79261258e-07, 9.98343097e-08, 2.69382872e-09],
              [2.76931189e-07, 1.00610364e-07, 2.64924727e-09],
              [2.61743025e-07, 9.72737908e-08, 2.82410162e-09],
              [2.45267103e-07, 9.24204926e-08, 2.92052182e-09]]], dtype=float32)

    • Jb
      (depth, hour, tau_bins)
      float32
      2.555e-10 2.914e-10 ... 2.291e-11

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[2.55470034e-10, 2.91401792e-10, 3.19249349e-10],
              [2.56496713e-10, 2.91035779e-10, 3.23981342e-10],
              [2.53829818e-10, 2.94654967e-10, 3.14947873e-10],
              [2.55533705e-10, 2.92805835e-10, 3.09960974e-10],
              [2.56380861e-10, 2.91983326e-10, 3.10544312e-10],
              [2.56754840e-10, 2.91529800e-10, 3.07330911e-10],
              [2.56076688e-10, 2.91074637e-10, 3.00592301e-10],
              [2.54780752e-10, 2.91799696e-10, 3.05651116e-10],
              [2.57439070e-10, 2.91535185e-10, 3.07487813e-10],
              [2.56943383e-10, 2.93832736e-10, 3.00166780e-10],
              [2.55828830e-10, 2.93334773e-10, 3.05036496e-10],
              [2.54726990e-10, 2.92834201e-10, 3.05739545e-10],
              [2.56902666e-10, 2.92254110e-10, 3.07121273e-10],
              [2.54530674e-10, 2.91644903e-10, 2.97666614e-10],
              [2.55004962e-10, 2.89583413e-10, 3.04066772e-10],
              [2.52027094e-10, 2.88339130e-10, 3.08847586e-10],
              [2.50817089e-10, 2.87278257e-10, 3.02292469e-10],
              [2.49159637e-10, 2.86964563e-10, 3.07309900e-10],
              [2.51137861e-10, 2.85814983e-10, 3.09010206e-10],
              [2.47247639e-10, 2.86292767e-10, 3.14035603e-10],
      ...
              [1.62041829e-08, 1.45443124e-08, 6.74942626e-08],
              [1.65704819e-08, 1.85746529e-08, 1.38406989e-07],
              [1.66631597e-08, 2.50191388e-08, 1.54171133e-07],
              [1.78619324e-08, 3.59505563e-08, 1.83110956e-07],
              [1.81895565e-08, 5.13915772e-08, 1.60862925e-07],
              [1.90937630e-08, 6.16821083e-08, 1.43145357e-07],
              [2.18083027e-08, 7.16589668e-08, 1.28102954e-07],
              [2.29776802e-08, 7.47350626e-08, 1.13467706e-07],
              [2.24974031e-08, 7.14504864e-08, 1.11496405e-07],
              [2.32104789e-08, 6.69645033e-08, 1.04462508e-07],
              [2.28019950e-08, 6.31832933e-08, 1.03584043e-07],
              [2.19223555e-08, 6.01868848e-08, 1.01363128e-07],
              [2.10841904e-08, 9.55064028e-09, 2.09327062e-08],
              [2.02724735e-08, 3.44046680e-09, 6.70337619e-10],
              [2.20876579e-08, 6.45958576e-09, 1.19146870e-09],
              [2.26566019e-08, 9.40468858e-09, 4.07481243e-10],
              [2.29477024e-08, 9.73539294e-09, 2.40757192e-10],
              [2.31211477e-08, 9.75570824e-09, 1.00751248e-10],
              [2.25524239e-08, 9.54282964e-09, 8.35792408e-11],
              [2.11913314e-08, 9.55753965e-09, 2.29118079e-11]]], dtype=float32)

    • Jq
      (depth, hour, tau_bins)
      float64
      -0.4038 -0.4617 ... -30.73 -1.899

      units :

      W/m^2

      long_name :

      $J_q^t$

      array([[[  -0.40383201,   -0.46170693,   -0.47979607],
              [  -0.40629385,   -0.46081924,   -0.49478992],
              [  -0.40548916,   -0.46611381,   -0.48753303],
              [  -0.40707212,   -0.46448855,   -0.49537153],
              [  -0.40654759,   -0.46543408,   -0.49638149],
              [  -0.40767024,   -0.46317764,   -0.5001483 ],
              [  -0.40825707,   -0.46347099,   -0.48866422],
              [  -0.40965568,   -0.46438217,   -0.48843255],
              [  -0.40948291,   -0.46580355,   -0.48770104],
              [  -0.40507042,   -0.46631911,   -0.48339054],
              [  -0.40664369,   -0.46244279,   -0.48619947],
              [  -0.39999109,   -0.46313942,   -0.49600014],
              [  -0.40572711,   -0.46118047,   -0.48885789],
              [  -0.40192261,   -0.46193756,   -0.47936888],
              [  -0.40096163,   -0.45710388,   -0.48437711],
              [  -0.39976277,   -0.45286841,   -0.4927702 ],
              [  -0.40124605,   -0.452229  ,   -0.48249731],
              [  -0.39830072,   -0.45048057,   -0.49243466],
              [  -0.40160454,   -0.44609005,   -0.4880124 ],
              [  -0.39683667,   -0.45253467,   -0.49126503],
      ...
              [ -38.01416094,  -37.71273213, -146.00726222],
              [ -38.08362599,  -42.7750946 , -298.59354522],
              [ -37.79889323,  -56.78291587, -339.93163012],
              [ -38.22386721,  -86.86501178, -375.49648732],
              [ -39.24619021, -127.04311749, -336.79308851],
              [ -41.1660171 , -157.00324937, -323.79881367],
              [ -44.60286042, -176.94763065, -294.54638861],
              [ -48.23935169, -180.63089509, -263.29843897],
              [ -49.37562729, -176.74255824, -248.19471826],
              [ -52.69931838, -165.18010348, -235.23162031],
              [ -53.43977151, -160.63160397, -234.23988518],
              [ -53.31057096, -152.06274918, -227.99312568],
              [ -49.36203681,  -33.31373377,  -62.13097584],
              [ -50.61750746,  -17.60716132,   -4.09462887],
              [ -53.39291811,  -30.18945973,   -5.85316103],
              [ -55.27374453,  -34.94254046,   -2.91233767],
              [ -55.69251915,  -34.25285055,   -2.41855112],
              [ -54.34907925,  -32.83374329,   -2.14437555],
              [ -52.16474975,  -31.96696915,   -2.31426363],
              [ -48.46496735,  -30.72726698,   -1.89948483]]])

    • S2
      (depth, hour, tau_bins)
      float32
      6.214e-05 0.0002147 ... 3.608e-05

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([[[6.2135194e-05, 2.1472209e-04, 3.2808020e-04],
              [6.2408239e-05, 2.1617881e-04, 3.8527348e-04],
              [6.0956540e-05, 2.2225048e-04, 3.8728234e-04],
              [6.1642189e-05, 2.1964675e-04, 4.0369909e-04],
              [5.8844664e-05, 2.1374767e-04, 4.1417207e-04],
              [5.7150966e-05, 2.0920373e-04, 4.1820243e-04],
              [5.5285251e-05, 2.0723182e-04, 4.0379536e-04],
              [5.5278397e-05, 2.0315332e-04, 4.3007903e-04],
              [5.6005490e-05, 2.0021363e-04, 4.3119671e-04],
              [5.7905778e-05, 2.0268056e-04, 4.2984835e-04],
              [5.8969716e-05, 2.0108584e-04, 4.9466669e-04],
              [5.7197434e-05, 2.0400004e-04, 4.8310720e-04],
              [5.8316582e-05, 2.0677842e-04, 4.4798007e-04],
              [5.5217875e-05, 2.0273292e-04, 3.9979507e-04],
              [5.2473857e-05, 1.9077447e-04, 3.7621343e-04],
              [5.0210474e-05, 1.8508767e-04, 3.5905137e-04],
              [4.9514329e-05, 1.7809295e-04, 3.6660919e-04],
              [4.7522713e-05, 1.7627905e-04, 3.5958833e-04],
              [4.7432528e-05, 1.7114768e-04, 3.4196809e-04],
              [4.7061854e-05, 1.7858839e-04, 3.7684879e-04],
      ...
              [1.6946094e-04, 1.5064844e-04, 9.2533010e-05],
              [1.7211071e-04, 1.6139646e-04, 8.4166153e-05],
              [1.7541717e-04, 1.7660802e-04, 6.0221559e-05],
              [1.7440240e-04, 1.9544957e-04, 3.2458531e-05],
              [1.8071404e-04, 1.9552017e-04, 2.2991680e-05],
              [1.8980163e-04, 1.7540010e-04, 1.7840273e-05],
              [1.9475482e-04, 1.4813445e-04, 1.3792065e-05],
              [2.0412008e-04, 1.0925530e-04, 1.1346765e-05],
              [2.0858954e-04, 7.4563985e-05, 9.1552747e-06],
              [2.0937597e-04, 6.3926171e-05, 8.1477992e-06],
              [2.0614298e-04, 5.4879973e-05, 7.9349493e-06],
              [2.0774821e-04, 4.9834987e-05, 7.8745115e-06],
              [2.2729186e-04, 1.0725283e-04, 7.3192496e-06],
              [2.2412286e-04, 1.3586249e-04, 1.1071936e-05],
              [2.1546050e-04, 1.4760980e-04, 1.6061604e-05],
              [2.1067094e-04, 1.4497816e-04, 1.9443167e-05],
              [2.0641519e-04, 1.3815981e-04, 2.6482619e-05],
              [2.0400202e-04, 1.3395854e-04, 3.0892639e-05],
              [1.9902988e-04, 1.3089213e-04, 3.5797952e-05],
              [1.9289140e-04, 1.2865067e-04, 3.6081707e-05]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float32
      0.0002396 0.0002716 ... 1.035e-05

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      array([[[2.39610745e-04, 2.71556084e-04, 2.70128832e-04],
              [2.40454014e-04, 2.70599645e-04, 2.74386490e-04],
              [2.38844208e-04, 2.72287260e-04, 2.75661034e-04],
              [2.39268178e-04, 2.72357342e-04, 2.73914455e-04],
              [2.38136767e-04, 2.70218472e-04, 2.74914317e-04],
              [2.40638648e-04, 2.65842449e-04, 2.78192281e-04],
              [2.39180474e-04, 2.64761271e-04, 2.83272122e-04],
              [2.38557084e-04, 2.67403608e-04, 2.80232227e-04],
              [2.40423135e-04, 2.67150201e-04, 2.80136708e-04],
              [2.40707173e-04, 2.69239477e-04, 2.72929028e-04],
              [2.40857858e-04, 2.67764030e-04, 2.78347288e-04],
              [2.40084250e-04, 2.69025011e-04, 2.78279331e-04],
              [2.41775560e-04, 2.68885138e-04, 2.73582235e-04],
              [2.38815803e-04, 2.68640055e-04, 2.75888189e-04],
              [2.39419605e-04, 2.66721123e-04, 2.76292703e-04],
              [2.39614528e-04, 2.65526905e-04, 2.78615538e-04],
              [2.39612215e-04, 2.63208203e-04, 2.79091299e-04],
              [2.38741486e-04, 2.62423680e-04, 2.80824257e-04],
              [2.38855631e-04, 2.61507288e-04, 2.79753702e-04],
              [2.38068678e-04, 2.64408678e-04, 2.79467757e-04],
      ...
              [4.28655549e-05, 3.03763100e-05, 1.97062764e-05],
              [4.30601140e-05, 3.16175210e-05, 1.90241481e-05],
              [4.39168252e-05, 3.30525654e-05, 1.80639163e-05],
              [4.37198396e-05, 3.46756715e-05, 1.31571032e-05],
              [4.35817747e-05, 3.43492829e-05, 9.33161937e-06],
              [4.41295670e-05, 3.12332850e-05, 7.67203892e-06],
              [4.47235834e-05, 2.71723238e-05, 6.19583034e-06],
              [4.54743567e-05, 2.19780250e-05, 5.31574733e-06],
              [4.57708011e-05, 1.73569697e-05, 4.65225321e-06],
              [4.60082410e-05, 1.53941182e-05, 4.28469411e-06],
              [4.54157052e-05, 1.37252200e-05, 4.09263794e-06],
              [4.43821191e-05, 1.29597693e-05, 4.18236550e-06],
              [4.52992681e-05, 1.17703157e-05, 3.95249845e-06],
              [4.71115054e-05, 1.77631937e-05, 3.62751052e-06],
              [4.60290321e-05, 2.39401288e-05, 4.93487551e-06],
              [4.58545837e-05, 2.56217081e-05, 5.63887988e-06],
              [4.60243464e-05, 2.62969115e-05, 7.22560526e-06],
              [4.58321811e-05, 2.66395800e-05, 8.58258500e-06],
              [4.60509145e-05, 2.69951252e-05, 9.79544802e-06],
              [4.51421438e-05, 2.74656868e-05, 1.03494922e-05]]], dtype=float32)

    • Rig
      (depth, hour, tau_bins)
      float32
      3.263 1.133 ... 0.2238 0.2969

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      array([[[3.2628639 , 1.132586  , 0.73984253],
              [3.2539244 , 1.1218415 , 0.6636562 ],
              [3.3077629 , 1.105064  , 0.657457  ],
              [3.199039  , 1.1237029 , 0.6516208 ],
              [3.407606  , 1.1350415 , 0.6307573 ],
              [3.551251  , 1.1491935 , 0.61930174],
              [3.7079988 , 1.1682833 , 0.6382069 ],
              [3.6843374 , 1.1969991 , 0.59546936],
              [3.6373034 , 1.1951932 , 0.58847064],
              [3.5286233 , 1.1990137 , 0.58950627],
              [3.5112283 , 1.2090486 , 0.5561185 ],
              [3.5832782 , 1.1928139 , 0.55407566],
              [3.6162767 , 1.197721  , 0.57535815],
              [3.6732585 , 1.2360073 , 0.6680939 ],
              [3.7434201 , 1.285768  , 0.6698258 ],
              [3.9210746 , 1.3179896 , 0.7200737 ],
              [4.1534557 , 1.3624804 , 0.72308815],
              [4.350609  , 1.3700106 , 0.72829914],
              [4.34737   , 1.4054053 , 0.74411196],
              [4.3841596 , 1.3435694 , 0.69021   ],
      ...
              [0.24822167, 0.2229091 , 0.22997776],
              [0.2502671 , 0.21606663, 0.2836377 ],
              [0.25094187, 0.20619413, 0.33462837],
              [0.25031978, 0.20203003, 0.39669967],
              [0.247252  , 0.20765834, 0.42320406],
              [0.24486798, 0.21742085, 0.43833   ],
              [0.2399987 , 0.22879753, 0.45103633],
              [0.2378625 , 0.24977483, 0.47837028],
              [0.23650162, 0.26490203, 0.50369376],
              [0.23604476, 0.27550876, 0.52109915],
              [0.2333923 , 0.27310133, 0.5113795 ],
              [0.23511393, 0.2857891 , 0.5116854 ],
              [0.19609238, 0.18043011, 0.53184634],
              [0.2021423 , 0.17112611, 0.33909   ],
              [0.20476255, 0.18330167, 0.29207003],
              [0.21014665, 0.19243236, 0.28591996],
              [0.21374545, 0.20258361, 0.2771893 ],
              [0.21875525, 0.21056795, 0.28172302],
              [0.2241939 , 0.21769346, 0.2848692 ],
              [0.23144257, 0.22380565, 0.29694226]]], dtype=float32)

    • Rig_T
      (depth, hour, tau_bins)
      float32
      3.261 1.081 0.6602 ... 0.1893 0.283

      long_name :

      $Ri^g_T$

      array([[[3.2613535 , 1.0807239 , 0.6601844 ],
              [3.2607088 , 1.0806876 , 0.6376095 ],
              [3.2541394 , 1.0787339 , 0.62403697],
              [3.1573467 , 1.095553  , 0.6195335 ],
              [3.33064   , 1.1045685 , 0.60020334],
              [3.4112148 , 1.1177173 , 0.57872057],
              [3.598929  , 1.1484542 , 0.59502447],
              [3.4866464 , 1.1638496 , 0.5739601 ],
              [3.478894  , 1.173312  , 0.5573975 ],
              [3.3537366 , 1.1519613 , 0.5667412 ],
              [3.2960985 , 1.1518707 , 0.5466292 ],
              [3.4085515 , 1.1364806 , 0.541849  ],
              [3.360477  , 1.1453516 , 0.56916815],
              [3.5060325 , 1.147816  , 0.64065945],
              [3.6237063 , 1.215729  , 0.6510779 ],
              [3.7974987 , 1.2563922 , 0.6879902 ],
              [4.037156  , 1.298629  , 0.6786159 ],
              [4.2002525 , 1.3074436 , 0.68557954],
              [4.2329836 , 1.3236547 , 0.71324885],
              [4.2080054 , 1.3151736 , 0.6461337 ],
      ...
              [0.19615808, 0.17713511, 0.17934614],
              [0.19791266, 0.16535255, 0.1786004 ],
              [0.19866161, 0.15758038, 0.18220627],
              [0.19679716, 0.14933537, 0.21518406],
              [0.19358617, 0.1441788 , 0.2393373 ],
              [0.18668334, 0.14489259, 0.255283  ],
              [0.18088251, 0.14877748, 0.27127427],
              [0.17411648, 0.15260799, 0.2903708 ],
              [0.16779342, 0.16068009, 0.30303138],
              [0.16678639, 0.16395637, 0.3173359 ],
              [0.16421464, 0.16863132, 0.3253452 ],
              [0.16048996, 0.17333059, 0.32974294],
              [0.15450153, 0.14349043, 0.34493858],
              [0.15779564, 0.14688559, 0.29000515],
              [0.1605247 , 0.1545775 , 0.25673643],
              [0.16559987, 0.16310802, 0.27112243],
              [0.17053735, 0.17055264, 0.2761741 ],
              [0.17479429, 0.1770179 , 0.27965385],
              [0.18131071, 0.18408577, 0.27241796],
              [0.18530713, 0.18929055, 0.2829873 ]]], dtype=float32)

    • tau
      (hour, tau_bins)
      float32
      0.02975 0.05414 ... 0.05437 0.08353

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([[0.02975217, 0.05414128, 0.08337677],
             [0.02997051, 0.05396286, 0.08334547],
             [0.02989443, 0.05379027, 0.08344196],
             [0.02965429, 0.05390463, 0.08429465],
             [0.02966079, 0.05406607, 0.08407371],
             [0.02973503, 0.05454572, 0.08423293],
             [0.02972214, 0.05489863, 0.08463559],
             [0.02960968, 0.05482671, 0.08451898],
             [0.02952631, 0.05462506, 0.08395986],
             [0.02949316, 0.05446932, 0.08439989],
             [0.02946503, 0.05439705, 0.08432356],
             [0.0292913 , 0.05438457, 0.08425272],
             [0.02905069, 0.05423319, 0.08445528],
             [0.02918139, 0.05503562, 0.08494085],
             [0.02949452, 0.0555568 , 0.08575647],
             [0.02982584, 0.05614248, 0.08632967],
             [0.03012207, 0.05678497, 0.08614163],
             [0.0303158 , 0.05744474, 0.08672108],
             [0.03049818, 0.05776399, 0.08675309],
             [0.03012105, 0.05725272, 0.08582553],
             [0.03001658, 0.05665065, 0.08521383],
             [0.03018327, 0.05611375, 0.08581671],
             [0.02999875, 0.05514082, 0.08425651],
             [0.02983666, 0.05437292, 0.08352954]], dtype=float32)

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ric=0.2, Ri0=0.5

  <xarray.DatasetView>
  Dimensions:   (depth: 6, hour: 24, tau_bins: 3)
  Coordinates:
    * depth     (depth) float64 -89.0 -69.0 -59.0 -49.0 -39.0 -29.0
      xh        float64 -140.0
      yh        float64 0.0625
      yq        float64 -0.0625
    * hour      (hour) int64 0 1 2 3 4 5 6 7 8 9 ... 14 15 16 17 18 19 20 21 22 23
    * tau_bins  (tau_bins) object (0.0, 0.04] (0.04, 0.075] (0.075, inf]
  Data variables:
      KT        (depth, hour, tau_bins) float32 1.001e-06 1.001e-06 ... 1.001e-06
      eps       (depth, hour, tau_bins) float32 1.181e-09 7.053e-08 ... 1.901e-09
      chi       (depth, hour, tau_bins) float32 2.421e-08 4.218e-08 ... 1.773e-11
      Jb        (depth, hour, tau_bins) float32 3.165e-10 4.063e-10 ... 5.549e-12
      Jq        (depth, hour, tau_bins) float64 -0.4816 -0.6392 ... -0.01328
      S2        (depth, hour, tau_bins) float32 0.0001418 0.0004672 ... 1.13e-05
      N2        (depth, hour, tau_bins) float32 0.0002504 0.0002843 ... 7.772e-06
      Rig       (depth, hour, tau_bins) float32 1.384 0.4356 ... 0.3581 0.6224
      Rig_T     (depth, hour, tau_bins) float32 1.343 0.4258 ... 0.297 0.5564
      tau       (hour, tau_bins) float32 0.02927 0.0545 ... 0.05481 0.08379
  Attributes:
      title:    KPP ν0=2.5, Ri0=0.5

  new_baseline.kpp.lmd.005

  • Groups: (0)
  • Dimensions:
    • depth: 6
    • hour: 24
    • tau_bins: 3
  • Coordinates: (6)
    • depth
      (depth)
      float64
      -89.0 -69.0 -59.0 -49.0 -39.0 -29.0

      axis :

      Z

      long_name :

      Interface pseudo-depth, -z*

      positive :

      up

      units :

      meter

      array([-89., -69., -59., -49., -39., -29.])

    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220, 222, 220, 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210, 258, 210, 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209, 257, 209, 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • hour
      (hour)
      int64
      0 1 2 3 4 5 6 ... 18 19 20 21 22 23

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23])

    • tau_bins
      (tau_bins)
      object
      (0.0, 0.04] ... (0.075, inf]

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([Interval(0.0, 0.04, closed='right'),
             Interval(0.04, 0.075, closed='right'),
             Interval(0.075, inf, closed='right')], dtype=object)

  • Data variables: (10)
    • KT
      (depth, hour, tau_bins)
      float32
      1.001e-06 1.001e-06 ... 1.001e-06

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062596e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062596e-06],
              [1.00062630e-06, 1.00062618e-06, 1.00062596e-06],
              [1.00062630e-06, 1.00062618e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062630e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062618e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062607e-06],
              [1.00062630e-06, 1.00062630e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062618e-06, 1.00062618e-06],
              [1.00062630e-06, 1.00062618e-06, 1.00062618e-06],
      ...
              [3.81811027e-04, 2.90885218e-04, 1.71408691e-02],
              [4.25665407e-04, 6.40261802e-04, 4.36169878e-02],
              [4.42612451e-04, 1.11911795e-03, 5.60436398e-02],
              [4.89161233e-04, 5.52093564e-03, 7.04844594e-02],
              [5.36391861e-04, 1.31646777e-02, 7.70594999e-02],
              [6.37809746e-04, 2.09450833e-02, 8.12212825e-02],
              [7.49726663e-04, 2.86364648e-02, 8.68151113e-02],
              [9.01583699e-04, 3.39822620e-02, 8.97497833e-02],
              [1.04411470e-03, 3.73836383e-02, 9.08122957e-02],
              [1.13334751e-03, 3.80024388e-02, 8.79565999e-02],
              [1.30286883e-03, 4.09585051e-02, 8.61921236e-02],
              [1.38791348e-03, 4.12570350e-02, 8.57141986e-02],
              [8.68165516e-04, 1.87469891e-03, 2.38270964e-02],
              [7.35677429e-04, 2.46586249e-04, 1.61692663e-03],
              [8.00757844e-04, 9.72932903e-05, 2.39668181e-03],
              [7.69015285e-04, 3.98732591e-05, 6.91609748e-04],
              [7.04221777e-04, 2.61289642e-05, 3.45671833e-05],
              [6.36181561e-04, 8.17534328e-06, 1.58894795e-06],
              [5.70160919e-04, 3.65733058e-06, 1.00068951e-06],
              [5.23375580e-04, 1.35793562e-06, 1.00068837e-06]]], dtype=float32)

    • eps
      (depth, hour, tau_bins)
      float32
      1.181e-09 7.053e-08 ... 1.901e-09

      long_name :

      $ε$

      units :

      W/kg

      array([[[1.18064569e-09, 7.05251040e-08, 2.77265144e-07],
              [1.17697518e-09, 6.97652922e-08, 2.77346743e-07],
              [1.23723454e-09, 6.87601371e-08, 2.65645610e-07],
              [1.18744836e-09, 7.00886318e-08, 2.49900353e-07],
              [1.13138343e-09, 6.80724241e-08, 2.20856890e-07],
              [1.05763143e-09, 5.98049397e-08, 2.27534343e-07],
              [1.03238118e-09, 5.61502809e-08, 1.94415634e-07],
              [1.02243036e-09, 5.38770273e-08, 1.87493413e-07],
              [1.03555686e-09, 5.08423632e-08, 1.82726680e-07],
              [1.12074439e-09, 4.81827485e-08, 1.85923653e-07],
              [1.18264931e-09, 4.74369664e-08, 1.98140725e-07],
              [1.21930477e-09, 4.99082411e-08, 1.98121114e-07],
              [1.21355803e-09, 5.19479499e-08, 1.89243195e-07],
              [1.17654253e-09, 4.82414180e-08, 1.89402016e-07],
              [1.13012932e-09, 4.11239540e-08, 1.79092623e-07],
              [1.09471476e-09, 3.74310609e-08, 1.71340531e-07],
              [1.06548481e-09, 3.41670372e-08, 2.20125287e-07],
              [1.02419428e-09, 3.14334869e-08, 2.32289850e-07],
              [9.57743884e-10, 2.92911473e-08, 2.34592022e-07],
              [9.83565784e-10, 3.88315229e-08, 2.46494466e-07],
      ...
              [7.89389105e-08, 8.09131961e-08, 6.12292922e-07],
              [8.69531078e-08, 2.28870036e-07, 7.79314803e-07],
              [9.55727728e-08, 4.57868623e-07, 7.80822688e-07],
              [1.06504153e-07, 6.26281007e-07, 6.89955129e-07],
              [1.33650801e-07, 6.77400521e-07, 5.93021298e-07],
              [1.84921817e-07, 6.60766887e-07, 4.93917923e-07],
              [2.31475937e-07, 6.05025548e-07, 4.17285662e-07],
              [2.99835392e-07, 5.35225240e-07, 3.60747038e-07],
              [3.61126069e-07, 4.55689417e-07, 3.15176209e-07],
              [3.34447122e-07, 4.01052773e-07, 2.99641272e-07],
              [3.45345427e-07, 3.78920788e-07, 2.82839551e-07],
              [3.29040120e-07, 3.50103562e-07, 2.86943362e-07],
              [1.45253949e-07, 6.14728890e-08, 9.53904760e-08],
              [1.38159805e-07, 1.47678936e-08, 1.28765443e-08],
              [1.49180920e-07, 1.29572850e-08, 1.89057943e-08],
              [1.46155969e-07, 1.01477031e-08, 1.00389563e-08],
              [1.36401056e-07, 8.72085781e-09, 3.28854233e-09],
              [1.22148748e-07, 7.87655008e-09, 1.59576929e-09],
              [1.08910697e-07, 7.31133554e-09, 1.24768662e-09],
              [9.78104424e-08, 7.43829576e-09, 1.90076577e-09]]], dtype=float32)

    • chi
      (depth, hour, tau_bins)
      float32
      2.421e-08 4.218e-08 ... 1.773e-11

      long_name :

      $χ$

      units :

      C^2/s

      array([[[2.42052565e-08, 4.21824211e-08, 6.13486506e-08],
              [2.43459812e-08, 4.24095639e-08, 6.18638438e-08],
              [2.49258587e-08, 4.28722551e-08, 6.38544719e-08],
              [2.41345166e-08, 4.36538734e-08, 6.12625897e-08],
              [2.41304896e-08, 4.32199982e-08, 6.58363177e-08],
              [2.39560300e-08, 4.23458459e-08, 6.48738592e-08],
              [2.39092675e-08, 4.18693453e-08, 6.48294289e-08],
              [2.36714595e-08, 4.21219220e-08, 6.36250235e-08],
              [2.35294735e-08, 4.19933741e-08, 6.35801527e-08],
              [2.38001778e-08, 4.14795842e-08, 6.10450712e-08],
              [2.42841285e-08, 4.09588949e-08, 6.14152711e-08],
              [2.45673526e-08, 4.12250003e-08, 6.06932744e-08],
              [2.50661039e-08, 4.04394562e-08, 6.33500861e-08],
              [2.45900935e-08, 4.08110026e-08, 6.14272153e-08],
              [2.40473650e-08, 3.92472401e-08, 6.08052844e-08],
              [2.33138522e-08, 3.92737824e-08, 5.67299274e-08],
              [2.31825688e-08, 3.83459664e-08, 5.78424597e-08],
              [2.29814709e-08, 3.78962568e-08, 5.81310964e-08],
              [2.24128005e-08, 3.67799586e-08, 5.87707021e-08],
              [2.26952821e-08, 3.83477641e-08, 5.96626819e-08],
      ...
              [6.58048833e-08, 3.45897888e-08, 3.06971515e-07],
              [7.74475311e-08, 1.30058410e-07, 4.16388218e-07],
              [8.59298765e-08, 2.74952754e-07, 3.99758875e-07],
              [9.98236942e-08, 3.31715938e-07, 2.91729918e-07],
              [1.24177078e-07, 3.48626457e-07, 2.22114693e-07],
              [1.59281512e-07, 3.29062061e-07, 1.67315164e-07],
              [1.86461662e-07, 2.82267621e-07, 1.27866073e-07],
              [2.02538843e-07, 2.26290396e-07, 9.58634558e-08],
              [2.10553225e-07, 1.79261093e-07, 8.23487909e-08],
              [1.90766357e-07, 1.44952992e-07, 7.88916452e-08],
              [1.84443806e-07, 1.37191520e-07, 7.31033865e-08],
              [1.62389298e-07, 1.18587856e-07, 7.63602017e-08],
              [8.45517505e-08, 9.69671721e-09, 2.11790869e-08],
              [7.38182564e-08, 1.38504741e-09, 1.56564184e-09],
              [1.16998599e-07, 9.71107417e-10, 3.63399155e-09],
              [1.27962963e-07, 5.68661396e-10, 2.04201012e-09],
              [1.21315836e-07, 4.69920158e-10, 1.60213856e-10],
              [1.14172195e-07, 1.94918595e-10, 2.22373005e-11],
              [1.04255875e-07, 1.51198110e-10, 1.14214176e-11],
              [9.37985618e-08, 1.06092787e-10, 1.77340642e-11]]], dtype=float32)

    • Jb
      (depth, hour, tau_bins)
      float32
      3.165e-10 4.063e-10 ... 5.549e-12

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_diffusive_buoyancy_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([[[3.16469878e-10, 4.06335215e-10, 4.95005370e-10],
              [3.17809973e-10, 4.08817480e-10, 5.05972153e-10],
              [3.19277160e-10, 4.06790518e-10, 5.20859522e-10],
              [3.16120546e-10, 4.13883317e-10, 5.00933961e-10],
              [3.13139681e-10, 4.10488865e-10, 5.20764376e-10],
              [3.10633408e-10, 4.08861972e-10, 5.17567100e-10],
              [3.07887188e-10, 4.05906087e-10, 5.04892905e-10],
              [3.05545256e-10, 4.04478839e-10, 5.07665687e-10],
              [3.04483549e-10, 4.05523504e-10, 5.02378528e-10],
              [3.06875358e-10, 4.05313921e-10, 4.88781682e-10],
              [3.09053727e-10, 4.06532308e-10, 4.90021246e-10],
              [3.14069798e-10, 4.04227846e-10, 4.88690088e-10],
              [3.19417853e-10, 4.00812328e-10, 4.95343877e-10],
              [3.16587395e-10, 4.00275202e-10, 4.89477736e-10],
              [3.14177545e-10, 3.95527555e-10, 4.95618879e-10],
              [3.08587988e-10, 3.92145816e-10, 4.73352690e-10],
              [3.05383857e-10, 3.87099630e-10, 4.77537287e-10],
              [3.04662073e-10, 3.82146120e-10, 4.79010498e-10],
              [3.03895215e-10, 3.79181186e-10, 4.81312434e-10],
              [3.04242853e-10, 3.84696941e-10, 4.91413632e-10],
      ...
              [3.82668297e-09, 2.23462582e-09, 1.09350111e-07],
              [5.13596010e-09, 1.12433076e-08, 1.73453998e-07],
              [6.16254914e-09, 2.75167942e-08, 1.78586021e-07],
              [7.04082215e-09, 5.64420901e-08, 1.63006959e-07],
              [9.40332612e-09, 8.36426182e-08, 1.39608161e-07],
              [1.21739756e-08, 9.57661683e-08, 1.16583863e-07],
              [1.44949128e-08, 9.81782833e-08, 1.04734198e-07],
              [1.82505584e-08, 9.62569473e-08, 9.39466602e-08],
              [2.09147721e-08, 8.83932714e-08, 8.06017368e-08],
              [2.07831228e-08, 7.65039445e-08, 8.95265799e-08],
              [2.29593340e-08, 7.25662659e-08, 7.88470302e-08],
              [2.28492016e-08, 6.55638672e-08, 8.51448192e-08],
              [7.32230188e-09, 3.86669319e-09, 2.06859028e-08],
              [5.07420683e-09, 1.86237886e-10, 6.27351116e-10],
              [8.44772785e-09, 7.74282166e-11, 2.03356487e-09],
              [9.28062427e-09, 1.02767976e-11, 6.27712660e-10],
              [8.80577300e-09, 6.66913183e-12, 3.52873321e-11],
              [8.48780868e-09, 5.79612356e-12, 6.17024704e-12],
              [7.54977414e-09, 5.89862187e-12, 4.79358827e-12],
              [6.58689014e-09, 6.02329558e-12, 5.54850436e-12]]], dtype=float32)

    • Jq
      (depth, hour, tau_bins)
      float64
      -0.4816 -0.6392 ... -0.01328

      units :

      W/m^2

      long_name :

      $J_q^t$

      array([[[-4.81552918e-01, -6.39167771e-01, -7.62933746e-01],
              [-4.85329101e-01, -6.42421775e-01, -7.69579102e-01],
              [-4.90946456e-01, -6.40105755e-01, -7.79354780e-01],
              [-4.83578128e-01, -6.47227242e-01, -7.73838051e-01],
              [-4.83167888e-01, -6.42581872e-01, -7.91665006e-01],
              [-4.80104343e-01, -6.40745775e-01, -7.90478163e-01],
              [-4.77434282e-01, -6.38051315e-01, -7.81818421e-01],
              [-4.75500770e-01, -6.35332332e-01, -7.90931913e-01],
              [-4.73519131e-01, -6.37496725e-01, -7.95123686e-01],
              [-4.78122582e-01, -6.36972344e-01, -7.80516887e-01],
              [-4.82024922e-01, -6.37689752e-01, -7.90518737e-01],
              [-4.84282250e-01, -6.35676375e-01, -7.79288919e-01],
              [-4.88477602e-01, -6.29759740e-01, -7.87195326e-01],
              [-4.83797879e-01, -6.26795017e-01, -7.73909660e-01],
              [-4.78714276e-01, -6.17771603e-01, -7.74951022e-01],
              [-4.71088650e-01, -6.12925103e-01, -7.50714301e-01],
              [-4.70555356e-01, -6.07242223e-01, -7.45696696e-01],
              [-4.70414981e-01, -6.04348376e-01, -7.45931460e-01],
              [-4.67814145e-01, -5.97328799e-01, -7.44359541e-01],
              [-4.64213041e-01, -6.07979658e-01, -7.65086306e-01],
      ...
              [-1.52504881e+01, -1.08198466e+01, -2.40912335e+02],
              [-1.79780728e+01, -2.77116033e+01, -4.22594618e+02],
              [-1.87859441e+01, -6.95042618e+01, -4.35070729e+02],
              [-2.05550840e+01, -1.68302990e+02, -4.19219828e+02],
              [-2.43782413e+01, -2.28539469e+02, -3.79352246e+02],
              [-2.99324167e+01, -2.53416225e+02, -3.39421920e+02],
              [-3.62329256e+01, -2.54032476e+02, -3.03281977e+02],
              [-4.57524427e+01, -2.37408954e+02, -2.80372229e+02],
              [-5.52564399e+01, -2.23234615e+02, -2.56960106e+02],
              [-5.82335571e+01, -2.00110950e+02, -2.44827332e+02],
              [-6.62942474e+01, -1.96551320e+02, -2.38200016e+02],
              [-7.06634387e+01, -1.84589225e+02, -2.36158794e+02],
              [-2.57588716e+01, -1.98516862e+01, -7.16579682e+01],
              [-2.11297949e+01, -2.07265483e+00, -6.26872271e+00],
              [-2.92281227e+01, -1.13948511e+00, -1.02573476e+01],
              [-2.99932999e+01, -5.12616250e-01, -4.02535216e+00],
              [-2.89512937e+01, -3.15996434e-01, -2.26048104e-01],
              [-2.69464025e+01, -1.24594175e-01, -1.93938576e-02],
              [-2.45196400e+01, -7.30878555e-02, -1.04230675e-02],
              [-2.18747312e+01, -4.18680368e-02, -1.32777846e-02]]])

    • S2
      (depth, hour, tau_bins)
      float32
      0.0001418 0.0004672 ... 1.13e-05

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([[[1.41836601e-04, 4.67205304e-04, 7.15640199e-04],
              [1.41885423e-04, 4.72498999e-04, 7.31437409e-04],
              [1.48016523e-04, 4.70162660e-04, 7.41417753e-04],
              [1.40774020e-04, 4.72693850e-04, 7.54935376e-04],
              [1.37023657e-04, 4.75256471e-04, 6.97829120e-04],
              [1.28246960e-04, 4.59599047e-04, 7.07112486e-04],
              [1.25261853e-04, 4.47610510e-04, 6.60455436e-04],
              [1.26983170e-04, 4.52060514e-04, 6.61135535e-04],
              [1.30664936e-04, 4.44407866e-04, 6.85080537e-04],
              [1.37154537e-04, 4.38653689e-04, 6.94098591e-04],
              [1.42129575e-04, 4.40310774e-04, 7.55701447e-04],
              [1.44565871e-04, 4.36307222e-04, 7.79487425e-04],
              [1.45691098e-04, 4.47019353e-04, 7.87896803e-04],
              [1.44280799e-04, 4.34455666e-04, 7.75266206e-04],
              [1.38319811e-04, 4.21505451e-04, 7.35988317e-04],
              [1.30991262e-04, 4.10690991e-04, 7.31264823e-04],
              [1.23740640e-04, 3.95052601e-04, 7.36095477e-04],
              [1.19035845e-04, 3.91300389e-04, 7.24209938e-04],
              [1.12783542e-04, 3.89472814e-04, 7.19981967e-04],
              [1.15830204e-04, 4.13761125e-04, 7.08727632e-04],
      ...
              [1.48438034e-04, 8.40546709e-05, 3.76138487e-05],
              [1.49744868e-04, 8.34067832e-05, 2.62681206e-05],
              [1.55048183e-04, 8.11126229e-05, 1.71083029e-05],
              [1.57887640e-04, 7.14546768e-05, 1.13211072e-05],
              [1.63779288e-04, 5.51105259e-05, 8.39280438e-06],
              [1.68770872e-04, 4.05372193e-05, 6.78651486e-06],
              [1.68515748e-04, 2.62506983e-05, 5.61330125e-06],
              [1.69917534e-04, 1.88715403e-05, 4.70684881e-06],
              [1.62872253e-04, 1.40121865e-05, 4.11091787e-06],
              [1.63078847e-04, 1.16760530e-05, 3.64465927e-06],
              [1.55001995e-04, 1.02589747e-05, 3.68917995e-06],
              [1.50725929e-04, 9.34424224e-06, 3.74268780e-06],
              [1.62076351e-04, 1.31826091e-05, 3.24889447e-06],
              [1.72409025e-04, 1.79971466e-05, 4.08658934e-06],
              [1.71353196e-04, 2.51919682e-05, 4.80949393e-06],
              [1.65656005e-04, 3.08949420e-05, 5.40391466e-06],
              [1.60071344e-04, 3.37284146e-05, 6.34383969e-06],
              [1.54592650e-04, 3.72321556e-05, 7.46843898e-06],
              [1.50900625e-04, 4.07821572e-05, 8.78149694e-06],
              [1.46861552e-04, 4.46950471e-05, 1.12951284e-05]]], dtype=float32)

    • N2
      (depth, hour, tau_bins)
      float32
      0.0002504 0.0002843 ... 7.772e-06

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Buoyancy frequency squared

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      s-2

      array([[[2.50380457e-04, 2.84322537e-04, 3.26497160e-04],
              [2.49794335e-04, 2.84366863e-04, 3.40093946e-04],
              [2.50732730e-04, 2.82385619e-04, 3.52266827e-04],
              [2.48224998e-04, 2.84529960e-04, 3.56787234e-04],
              [2.50212091e-04, 2.83362053e-04, 3.48632922e-04],
              [2.50026409e-04, 2.83005007e-04, 3.47889756e-04],
              [2.52470782e-04, 2.82140682e-04, 3.33684904e-04],
              [2.52225524e-04, 2.83426692e-04, 3.28114023e-04],
              [2.52644822e-04, 2.83705245e-04, 3.10908479e-04],
              [2.55907566e-04, 2.84160778e-04, 2.99137144e-04],
              [2.56487081e-04, 2.83852918e-04, 2.97512830e-04],
              [2.59825640e-04, 2.79677886e-04, 3.08105693e-04],
              [2.58581596e-04, 2.79740925e-04, 3.08726507e-04],
              [2.58143933e-04, 2.80908833e-04, 3.07591661e-04],
              [2.53710255e-04, 2.79614876e-04, 3.14174657e-04],
              [2.53522012e-04, 2.79249914e-04, 3.10601346e-04],
              [2.52498110e-04, 2.75294151e-04, 3.15695826e-04],
              [2.51230667e-04, 2.75489874e-04, 3.10448901e-04],
              [2.47999094e-04, 2.76833016e-04, 3.11023148e-04],
              [2.47006770e-04, 2.79002707e-04, 3.11012758e-04],
      ...
              [4.00318095e-05, 2.19094200e-05, 1.75809691e-05],
              [4.08545384e-05, 2.29993839e-05, 1.44682808e-05],
              [4.17602168e-05, 2.32393431e-05, 1.14376789e-05],
              [4.19672360e-05, 2.17431643e-05, 7.65365985e-06],
              [4.25652397e-05, 1.95154098e-05, 5.98406086e-06],
              [4.24695463e-05, 1.64385110e-05, 5.06878587e-06],
              [4.20580618e-05, 1.29026885e-05, 4.27718123e-06],
              [4.17472147e-05, 9.78873140e-06, 3.76438538e-06],
              [4.05107130e-05, 7.85583507e-06, 3.40888255e-06],
              [3.98970660e-05, 6.68765961e-06, 3.17039030e-06],
              [3.72795621e-05, 6.00186786e-06, 3.05350545e-06],
              [3.63023901e-05, 5.69715758e-06, 3.10032669e-06],
              [3.58221514e-05, 5.26593840e-06, 2.91269725e-06],
              [3.86316751e-05, 5.47839863e-06, 2.88081537e-06],
              [4.08812521e-05, 7.10814311e-06, 3.56441478e-06],
              [4.26108927e-05, 8.30356112e-06, 4.09821496e-06],
              [4.13776215e-05, 9.73325223e-06, 4.94596861e-06],
              [4.03243102e-05, 1.09723651e-05, 5.75752665e-06],
              [4.00129029e-05, 1.23583932e-05, 6.63946867e-06],
              [3.99043274e-05, 1.35408591e-05, 7.77176410e-06]]], dtype=float32)

    • Rig
      (depth, hour, tau_bins)
      float32
      1.384 0.4356 ... 0.3581 0.6224

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      $Ri^g$

      time_avg_info :

      average_T1,average_T2,average_DT

      array([[[1.3843784 , 0.4355776 , 0.3091246 ],
              [1.3862953 , 0.42921245, 0.29979953],
              [1.3186188 , 0.43328127, 0.29791135],
              [1.319762  , 0.431867  , 0.31552285],
              [1.3876023 , 0.42956513, 0.33238146],
              [1.4713855 , 0.43854415, 0.33611113],
              [1.4585589 , 0.44340074, 0.3397169 ],
              [1.422827  , 0.44158596, 0.33740765],
              [1.4308827 , 0.44296315, 0.3283518 ],
              [1.3624867 , 0.44709092, 0.32959506],
              [1.3321421 , 0.44995126, 0.31895676],
              [1.3095046 , 0.44489077, 0.31756058],
              [1.299477  , 0.44661266, 0.31964993],
              [1.3353751 , 0.45561314, 0.32711515],
              [1.3878534 , 0.46209648, 0.33242503],
              [1.4295163 , 0.47146723, 0.3417432 ],
              [1.5292119 , 0.48259148, 0.33962864],
              [1.5888655 , 0.49773607, 0.3420213 ],
              [1.6786402 , 0.50815296, 0.34517553],
              [1.6653159 , 0.49143514, 0.32707235],
      ...
              [0.28946295, 0.31139803, 0.48474592],
              [0.28830624, 0.31103155, 0.6091647 ],
              [0.28619725, 0.31105167, 0.6668595 ],
              [0.27883843, 0.3508357 , 0.70064497],
              [0.27638355, 0.40184402, 0.73615754],
              [0.2694257 , 0.4452605 , 0.747144  ],
              [0.2700301 , 0.5024443 , 0.76513684],
              [0.27028793, 0.53571266, 0.8011063 ],
              [0.27429098, 0.5733974 , 0.8354088 ],
              [0.27542618, 0.5895082 , 0.85250646],
              [0.27886292, 0.59905994, 0.8383981 ],
              [0.27949739, 0.61504954, 0.85223484],
              [0.23919442, 0.4293044 , 0.8984655 ],
              [0.23977003, 0.3274899 , 0.7073171 ],
              [0.24828433, 0.31492984, 0.70929885],
              [0.2581412 , 0.31994987, 0.7032059 ],
              [0.2622922 , 0.32738152, 0.69430584],
              [0.26598185, 0.33688056, 0.6817956 ],
              [0.27302545, 0.34765816, 0.68264323],
              [0.28132206, 0.35813454, 0.6223506 ]]], dtype=float32)

    • Rig_T
      (depth, hour, tau_bins)
      float32
      1.343 0.4258 0.304 ... 0.297 0.5564

      long_name :

      $Ri^g_T$

      array([[[1.3434284 , 0.42575198, 0.30399483],
              [1.339348  , 0.42032528, 0.2998174 ],
              [1.2688062 , 0.42568558, 0.3012114 ],
              [1.2518506 , 0.42318174, 0.3039454 ],
              [1.3326304 , 0.42496133, 0.3102635 ],
              [1.4218832 , 0.43632212, 0.31440943],
              [1.384247  , 0.44214174, 0.33351892],
              [1.3839062 , 0.4455327 , 0.32989636],
              [1.3978841 , 0.4521952 , 0.32683736],
              [1.3210678 , 0.4536286 , 0.31877822],
              [1.2479538 , 0.4600122 , 0.3171023 ],
              [1.2329819 , 0.45810977, 0.31853944],
              [1.244715  , 0.45624778, 0.31939033],
              [1.2971063 , 0.46135846, 0.32418156],
              [1.3399007 , 0.46895933, 0.33118114],
              [1.3868494 , 0.47377062, 0.34303665],
              [1.536852  , 0.4890711 , 0.33796883],
              [1.5975964 , 0.49740615, 0.33952767],
              [1.6472256 , 0.50301504, 0.33876863],
              [1.6406693 , 0.49364513, 0.3232821 ],
      ...
              [0.21892303, 0.24460581, 0.2782022 ],
              [0.21715575, 0.23441526, 0.3032993 ],
              [0.21339254, 0.22937962, 0.33986917],
              [0.20731284, 0.22571316, 0.3779726 ],
              [0.20430309, 0.23617734, 0.4060605 ],
              [0.19837219, 0.24865082, 0.4239027 ],
              [0.19388697, 0.27108073, 0.44667554],
              [0.19081205, 0.2972318 , 0.46966553],
              [0.18402362, 0.32012552, 0.47574738],
              [0.18582553, 0.3224433 , 0.5086578 ],
              [0.18361391, 0.34084806, 0.52148837],
              [0.18255128, 0.34092087, 0.52430046],
              [0.17780563, 0.27581286, 0.59936225],
              [0.18213266, 0.26158136, 0.53423965],
              [0.18586144, 0.25454444, 0.54415107],
              [0.19341025, 0.26411104, 0.5709722 ],
              [0.19832414, 0.2755742 , 0.5958633 ],
              [0.20684622, 0.28239402, 0.59805214],
              [0.21064335, 0.29060137, 0.5932599 ],
              [0.2162599 , 0.29699275, 0.5563707 ]]], dtype=float32)

    • tau
      (hour, tau_bins)
      float32
      0.02927 0.0545 ... 0.05481 0.08379

      cell_methods :

      yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Zonal surface stress from ocean interactions with atmos and ice

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      Pa

      array([[0.02926666, 0.05449591, 0.08384066],
             [0.02953365, 0.05412898, 0.08342438],
             [0.02950766, 0.0540043 , 0.08344106],
             [0.02929587, 0.05410497, 0.08466474],
             [0.0292785 , 0.05410444, 0.08424635],
             [0.02926119, 0.05435768, 0.08391857],
             [0.02946588, 0.05500093, 0.08486678],
             [0.02949753, 0.05470305, 0.08457853],
             [0.02931711, 0.05448961, 0.08386523],
             [0.02923332, 0.05441001, 0.08456027],
             [0.02923119, 0.05438141, 0.08475135],
             [0.02906845, 0.05451858, 0.0846531 ],
             [0.02867212, 0.05450471, 0.08504945],
             [0.02912105, 0.05511454, 0.0853009 ],
             [0.02948293, 0.05570985, 0.08595277],
             [0.0298175 , 0.05617661, 0.08633688],
             [0.030358  , 0.0568147 , 0.08630857],
             [0.0307796 , 0.05742799, 0.08671759],
             [0.03049245, 0.05769216, 0.08660597],
             [0.0303795 , 0.05715569, 0.08534982],
             [0.03001719, 0.05657351, 0.08543649],
             [0.03012108, 0.05630453, 0.08567342],
             [0.02991865, 0.05550613, 0.08454789],
             [0.02963275, 0.05481243, 0.08379018]], dtype=float32)

  • Attributes: (1)

    title :

    KPP ν0=2.5, Ri0=0.5

• Dimensions:
• Coordinates: (0)
• Data variables: (0)
• Attributes: (0)

euc relative frame

# could be cleaned up
newtree = mixpods.bin_to_euc_centered_coordinate(tree)
# merge tree
for nodename, _ in tree.children.items():
    tree[f"{nodename}/euc"] = newtree[f"{nodename}/euc"]

euc_mean = mixpods.average_euc(newtree)
for nodename, _ in tree.children.items():
    tree[f"{nodename}/euc/mean"] = euc_mean[f"{nodename}"]

euc_subset = DataTree()
for nodename, _ in tree.children.items():
    euc_subset[f"{nodename}"] = tree[f"{nodename}/euc"]

euc_subset.to_zarr("mom6-euc-coordinate.zarr")

<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast

euc = datatree.open_datatree("mom6-euc-coordinate.zarr", engine="zarr")
euc_mean = euc.dc.extract_leaf("mean")
tree = tree.dc.insert_as_subtree("euc", euc)

Merge in microstructure

micro_zeuc = datatree.open_datatree(
    os.path.expanduser("~/datasets/microstructure/equix-tiwe-zeuc.average.nc")
).load()
for nodename, node in micro_zeuc.children.items():
    node["u"].attrs["standard_name"] = "sea_water_x_velocity"
    node["v"].attrs["standard_name"] = "sea_water_y_velocity"
    node["KT"].attrs["standard_name"] = "ocean_vertical_heat_diffusivity"
    node["ν"].attrs["standard_name"] = "ocean_vertical_momentum_diffusivity"

    del node["Rig_T"].attrs["standard_name"]
    del node["N2T"].attrs["standard_name"]
    # euc_mean[nodename] = node

for nodename, node in micro_zeuc.children.items():
    euc_mean[nodename] = node

Validate before continuing

mixpods.validate_tree(tree)

Persist tree

tree = mixpods.persist_tree(tree)

euc_mean.load()

<xarray.DatasetView>
Dimensions:  ()
Data variables:
    *empty*

datatree.DataTree

• Groups: (8)
  <xarray.DatasetView>
  Dimensions:             (zeuc: 50)
  Coordinates:
      latitude            float32 0.0
      longitude           float32 -140.0
      reference_pressure  int64 0
    * zeuc                (zeuc) float64 -297.5 -287.5 -277.5 ... 182.5 192.5
  Data variables:
      u                   (zeuc) float32 nan nan nan ... -0.2604 -0.3176 nan
      v                   (zeuc) float32 nan nan nan ... -0.02813 0.02461 nan
      theta               (zeuc) float64 nan 11.76 11.79 ... 27.27 27.16 27.48
      S2                  (zeuc) float32 nan nan nan ... 3.625e-05 3.295e-05 nan
      Rig_T               (zeuc) float64 nan nan nan nan ... 0.09194 0.03173 nan
      Tflx_dia_diff       (zeuc) float64 nan nan nan nan ... nan nan nan
      KT                  (zeuc) float64 nan nan nan nan ... 0.00287 nan nan nan
      ν                   (zeuc) float64 nan nan nan nan nan ... nan nan nan nan
      chi                 (zeuc) float64 nan nan nan nan ... 1.624e-08 nan nan nan
      eps                 (zeuc) float64 nan nan nan nan ... 6.217e-08 nan nan nan

  TAO

  • Groups: (0)
  • Dimensions:
    • zeuc: 50
  • Coordinates: (4)
    • latitude
      ()
      float32
      0.0

      array(0., dtype=float32)

    • longitude
      ()
      float32
      -140.0

      array(-140., dtype=float32)

    • reference_pressure
      ()
      int64
      0

      array(0)

    • zeuc
      (zeuc)
      float64
      -297.5 -287.5 ... 182.5 192.5

      array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5,
             -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5,
             -117.5, -107.5,  -97.5,  -87.5,  -77.5,  -67.5,  -57.5,  -47.5,  -37.5,
              -27.5,  -17.5,   -7.5,    2.5,   12.5,   22.5,   32.5,   42.5,   52.5,
               62.5,   72.5,   82.5,   92.5,  102.5,  112.5,  122.5,  132.5,  142.5,
              152.5,  162.5,  172.5,  182.5,  192.5])

  • Data variables: (10)
    • u
      (zeuc)
      float32
      nan nan nan ... -0.2604 -0.3176 nan

      FORTRAN_format :

      epic_code :

      1205

      generic_name :

      u

      long_name :

      u

      name :

      u

      standard_name :

      sea_water_x_velocity

      units :

      m/s

      array([        nan,         nan,         nan, -0.34412488, -0.18681769,
             -0.13121113, -0.13110007, -0.13295907, -0.11932758, -0.08944993,
             -0.06136366, -0.02866847,  0.00694463,  0.04042118,  0.0743546 ,
              0.10901111,  0.14541332,  0.18769473,  0.23589374,  0.2907243 ,
              0.3542546 ,  0.42696685,  0.50931793,  0.6012157 ,  0.7011007 ,
              0.8074448 ,  0.91773796,  1.0287796 ,  1.1347147 ,  1.2393551 ,
              1.2138937 ,  1.0788711 ,  0.91834927,  0.70995677,  0.5181305 ,
              0.3438547 ,  0.18748318,  0.05523353, -0.04432773, -0.11947555,
             -0.17376517, -0.21139875, -0.24530154, -0.270233  , -0.24724326,
             -0.17648801, -0.07476283, -0.26038134, -0.31755555,         nan],
            dtype=float32)

    • v
      (zeuc)
      float32
      nan nan nan ... 0.02461 nan

      FORTRAN_format :

      epic_code :

      1206

      generic_name :

      v

      long_name :

      v

      name :

      v

      standard_name :

      sea_water_y_velocity

      units :

      m/s

      array([        nan,         nan,         nan,  0.04195916,  0.05380342,
              0.01274851,  0.00227688, -0.00924548, -0.01995938, -0.01586557,
             -0.01145717, -0.00865888, -0.00767306, -0.00643414, -0.00592971,
             -0.004652  , -0.00347026, -0.00338077, -0.00492731, -0.00728909,
             -0.00941122, -0.01004394, -0.00936652, -0.00851536, -0.00894195,
             -0.01160023, -0.01552894, -0.01913672, -0.02083625, -0.02033128,
             -0.01806714, -0.02482096, -0.03544367, -0.03519909, -0.03364132,
             -0.03147318, -0.030247  , -0.02728803, -0.02601177, -0.02645807,
             -0.02617701, -0.02455281, -0.02730868, -0.0510966 , -0.04896826,
             -0.01222637, -0.04429276, -0.02813384,  0.02461111,         nan],
            dtype=float32)

    • theta
      (zeuc)
      float64
      nan 11.76 11.79 ... 27.16 27.48

      description :

      potential temperature using T, S=35

      long_name :

      $θ$

      standard_name :

      sea_water_potential_temperature

      units :

      degC

      array([        nan, 11.75736668, 11.79488486, 11.84763801, 11.93816873,
             12.05439489, 12.13125832, 12.06222168, 11.96635654, 11.9495776 ,
             11.98335018, 12.04190823, 12.1266774 , 12.23353507, 12.36732257,
             12.53790349, 12.74391642, 12.98199528, 13.24475614, 13.53098288,
             13.84172953, 14.17438081, 14.55655615, 15.0141248 , 15.57278846,
             16.24306478, 17.04445091, 17.97797458, 19.09056328, 20.27529103,
             21.49486726, 22.6751356 , 23.67650756, 24.39508511, 24.92568977,
             25.29087219, 25.54098213, 25.70651579, 25.81024759, 25.87502808,
             25.94671359, 26.06974854, 26.271824  , 26.51445195, 26.76821484,
             26.96498047, 27.0757443 , 27.26711222, 27.1600157 , 27.47678427])

    • S2
      (zeuc)
      float32
      nan nan nan ... 3.295e-05 nan

      long_name :

      $S^2$

      array([           nan,            nan,            nan, 4.00262506e-05,
             1.99538299e-05, 2.05517135e-05, 2.50414141e-05, 2.88889587e-05,
             2.94069741e-05, 2.84150774e-05, 2.68097574e-05, 2.58364780e-05,
             2.66908228e-05, 2.83490008e-05, 3.06443253e-05, 3.34476608e-05,
             3.77655488e-05, 4.43660574e-05, 5.27910342e-05, 6.30350623e-05,
             7.58075330e-05, 9.16752906e-05, 1.10956047e-04, 1.29934648e-04,
             1.48486783e-04, 1.66213635e-04, 1.79030249e-04, 1.85601544e-04,
             1.88380363e-04, 1.38660631e-04, 1.41002165e-04, 5.10490616e-04,
             5.97267004e-04, 5.10636659e-04, 4.25692560e-04, 3.60526814e-04,
             3.14358331e-04, 2.77291780e-04, 2.28823061e-04, 1.92833613e-04,
             1.59598058e-04, 1.22315978e-04, 1.07536085e-04, 9.86665691e-05,
             8.83758039e-05, 4.32099259e-05, 3.16419428e-05, 3.62538267e-05,
             3.29506183e-05,            nan], dtype=float32)

    • Rig_T
      (zeuc)
      float64
      nan nan nan ... 0.09194 0.03173 nan

      long_name :

      $Ri^g_T$

      array([       nan,        nan,        nan,        nan,        nan,
             0.01489311, 0.03165001, 0.06313003, 0.14558223, 0.30405107,
             0.5510997 , 0.84501221, 1.00159469, 1.10171097, 1.17564499,
             1.25644404, 1.32506321, 1.30966974, 1.20444936, 1.09169364,
             1.01902732, 0.98161728, 0.93856368, 0.95847886, 1.0277866 ,
             1.14108322, 1.30324548, 1.53638172, 1.83930923, 3.08740971,
             5.92696916, 0.64201333, 0.39953276, 0.31802397, 0.27029139,
             0.2428626 , 0.22293594, 0.2077806 , 0.19648165, 0.18172446,
             0.16687778, 0.15812352, 0.15209324, 0.14781051, 0.16795355,
             0.18188397, 0.11292903, 0.09193901, 0.03173482,        nan])

    • Tflx_dia_diff
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan

      standard_name :

      ocean_vertical_diffusive_heat_flux

      array([            nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan, -5.17514235e-07,
                         nan, -2.82599747e-06, -3.66443945e-07, -6.14955296e-06,
             -3.76138545e-06, -2.92809353e-06, -6.45150609e-06, -9.99721035e-06,
             -1.10884138e-05, -1.18222800e-05, -1.43206993e-05, -1.53016769e-05,
             -1.85974551e-05, -1.64466029e-05, -1.42208123e-05, -1.48474476e-05,
             -1.59149521e-05, -1.12224783e-05, -7.01141757e-06, -7.11490467e-06,
             -4.62302402e-06, -3.49574049e-06, -4.14221714e-06,             nan,
                         nan,             nan])

    • KT
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan

      standard_name :

      ocean_vertical_heat_diffusivity

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan, 2.91855685e-05,
                        nan, 2.63724818e-04, 5.17551881e-05, 5.16996775e-04,
             2.38649440e-03, 4.78397845e-04, 4.52111916e-04, 6.96026866e-04,
             9.52619969e-04, 1.08718966e-03, 1.55516006e-03, 2.27080410e-02,
             2.70914629e-02, 6.55873581e-03, 4.65641779e-03, 2.04377327e-02,
             2.38048398e-02, 1.97063766e-02, 2.76059209e-03, 2.76672216e-03,
             2.04669252e-03, 2.47503727e-03, 2.86957564e-03,            nan,
                        nan,            nan])

    • ν
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      ocean_vertical_momentum_diffusivity

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan, 1.68499682e-05, 3.85379335e-05, 8.95743212e-04,
             6.61870626e-04, 1.25469635e-03, 1.04469512e-03, 4.82821120e-04,
             6.60574882e-04, 1.36210951e-03, 9.19967017e-04, 1.06437092e-03,
             1.72836434e-03, 2.17503465e-03, 2.81499143e-03, 4.14499709e-03,
             1.75873300e-02, 6.07257448e-03, 6.30236566e-03, 1.63667027e-02,
             2.24646176e-03, 4.97174757e-06,            nan,            nan,
                        nan,            nan])

    • chi
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan, 1.86298805e-08,
                        nan, 6.29706882e-08, 6.09176283e-08, 9.08369013e-07,
             4.42442169e-07, 4.39236144e-07, 1.11900126e-06, 1.65520285e-06,
             1.57006590e-06, 1.24562823e-06, 1.13238867e-06, 9.20620793e-07,
             8.59863821e-07, 6.37894873e-07, 4.62336468e-07, 4.36265962e-07,
             4.36628547e-07, 2.59989298e-07, 1.10137167e-07, 1.40344814e-07,
             5.56811423e-08, 1.42741529e-08, 1.62351688e-08,            nan,
                        nan,            nan])

    • eps
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan, 7.84301387e-09,
                        nan, 3.62689641e-08, 4.56603535e-09, 1.34903330e-07,
             8.03762811e-08, 6.24810991e-08, 1.41756879e-07, 2.40822512e-07,
             2.48318857e-07, 2.92684586e-07, 3.38003888e-07, 3.36031778e-07,
             4.38201807e-07, 3.46936120e-07, 2.95196047e-07, 3.14040551e-07,
             3.34615731e-07, 2.23966313e-07, 1.29571308e-07, 1.43364960e-07,
             9.57843724e-08, 5.78129980e-08, 6.21678115e-08,            nan,
                        nan,            nan])

  • Attributes: (0)

  <xarray.DatasetView>
  Dimensions:        (zeuc: 50)
  Coordinates:
      xh             float64 -140.0
      yh             float64 0.0625
      yq             float64 -0.0625
    * zeuc           (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5
  Data variables:
      uo             (zeuc) float32 0.005257 -0.0184 0.0179 ... nan nan nan
      vo             (zeuc) float32 -0.00127 1.53e-05 0.001303 ... nan nan nan
      ν              (zeuc) float32 0.0002048 0.0002048 0.0002047 ... nan nan nan
      thetao         (zeuc) float32 10.1 10.37 10.47 10.77 ... nan nan nan nan
      Tflx_dia_diff  (zeuc) float32 1.993e-08 2.055e-08 2.098e-08 ... nan nan nan
      Kd_heat        (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan
      chi            (zeuc) float32 7.983e-10 8.5e-10 8.842e-10 ... nan nan nan
      eps            (zeuc) float32 9.259e-11 1.049e-10 1.029e-10 ... nan nan nan
      S2             (zeuc) float32 2.532e-07 3.085e-07 2.927e-07 ... nan nan nan
      Rig_T          (zeuc) float32 215.0 202.1 206.4 200.1 ... nan nan nan nan

  baseline.001

  • Groups: (0)
  • Dimensions:
    • zeuc: 50
  • Coordinates: (4)
    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220 222 220 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210 258 210 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209 257 209 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zeuc
      (zeuc)
      float64
      -297.5 -287.5 ... 182.5 192.5

      array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5,
             -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5,
             -117.5, -107.5,  -97.5,  -87.5,  -77.5,  -67.5,  -57.5,  -47.5,  -37.5,
              -27.5,  -17.5,   -7.5,    2.5,   12.5,   22.5,   32.5,   42.5,   52.5,
               62.5,   72.5,   82.5,   92.5,  102.5,  112.5,  122.5,  132.5,  142.5,
              152.5,  162.5,  172.5,  182.5,  192.5])

  • Data variables: (10)
    • uo
      (zeuc)
      float32
      0.005257 -0.0184 0.0179 ... nan nan

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      array([ 0.00525669, -0.01840188,  0.01790354,  0.0044428 , -0.0186389 ,
              0.01406885,  0.0060294 , -0.01403717,  0.00815873,  0.00842694,
              0.01887635,  0.0139282 ,  0.02721404,  0.03995026,  0.0621833 ,
              0.08161839,  0.11901507,  0.14735334,  0.20004503,  0.23976822,
              0.32848406,  0.3751118 ,  0.4712556 ,  0.52341986,  0.5774437 ,
              0.7075572 ,  0.77591175,  0.8743804 ,  0.9184269 ,  1.0187924 ,
              0.9954761 ,  0.9605037 ,  0.7945212 ,  0.57813746,  0.44689173,
              0.33381617,  0.23095536,  0.12913482,  0.03992718, -0.02481052,
             -0.08560526, -0.15248425, -0.10183207, -0.08354276, -0.13608389,
                     nan,         nan,         nan,         nan,         nan],
            dtype=float32)

    • vo
      (zeuc)
      float32
      -0.00127 1.53e-05 ... nan nan

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      array([-1.2702544e-03,  1.5298558e-05,  1.3026360e-03, -1.1253332e-03,
             -8.5902464e-04,  1.3367438e-03, -1.2649365e-03, -1.2388808e-03,
              6.0991739e-04, -3.4798661e-05,  3.7417454e-03, -6.4203995e-03,
             -1.3410304e-04,  7.5575250e-04, -1.5261871e-03, -8.9544582e-04,
             -2.8911787e-03, -2.2536672e-03, -3.4135813e-03, -3.5160836e-03,
             -4.5007500e-03, -5.0586360e-03, -4.4486639e-03, -5.7875998e-03,
             -5.1613864e-03, -6.5229321e-03, -6.9063683e-03, -7.4243234e-03,
             -5.4123211e-03, -3.4078853e-03, -7.4119419e-03, -8.3914027e-03,
             -2.5121963e-03, -3.6355543e-03, -1.0095604e-02, -1.7872766e-02,
             -2.3629947e-02, -2.7661886e-02, -2.9444154e-02, -2.7525136e-02,
             -2.4786126e-02, -1.9260338e-02, -1.8448964e-02, -1.3063808e-01,
             -1.2816702e-01,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • ν
      (zeuc)
      float32
      0.0002048 0.0002048 ... nan nan

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([0.00020476, 0.00020475, 0.00020475, 0.00020478, 0.00020481,
             0.0002048 , 0.00020482, 0.00020483, 0.00020482, 0.00020483,
             0.00020483, 0.0002049 , 0.00020487, 0.00020489, 0.00020487,
             0.00020487, 0.0002049 , 0.00020492, 0.00020495, 0.00020499,
             0.0002054 , 0.00020508, 0.00020601, 0.00020516, 0.00020499,
             0.00020502, 0.00020504, 0.00020509, 0.00020512, 0.00020516,
             0.00020516, 0.00020649, 0.00034668, 0.00085228, 0.00146995,
             0.00216532, 0.0035074 , 0.00650569, 0.01049786, 0.01307183,
             0.01283239, 0.01135312, 0.0113987 , 0.01385058, 0.00340031,
                    nan,        nan,        nan,        nan,        nan],
            dtype=float32)

    • thetao
      (zeuc)
      float32
      10.1 10.37 10.47 ... nan nan nan

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([10.102968 , 10.372348 , 10.466407 , 10.773228 , 11.006616 ,
             11.139993 , 11.4113245, 11.583537 , 11.776898 , 11.990218 ,
             12.162569 , 12.333334 , 12.339282 , 12.497538 , 12.593491 ,
             12.730412 , 12.862454 , 13.050186 , 13.259371 , 13.581723 ,
             14.052092 , 14.471857 , 15.148092 , 15.4848175, 16.043379 ,
             16.859596 , 17.563826 , 18.593836 , 19.781422 , 20.625542 ,
             21.567474 , 22.509794 , 23.978546 , 25.125193 , 25.493336 ,
             25.699633 , 25.858055 , 25.986782 , 26.08607  , 26.141712 ,
             26.089527 , 25.978794 , 26.172031 , 25.693325 , 25.745844 ,
                    nan,        nan,        nan,        nan,        nan],
            dtype=float32)

    • Tflx_dia_diff
      (zeuc)
      float32
      1.993e-08 2.055e-08 ... nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      standard_name :

      ocean_vertical_diffusive_heat_flux

      array([1.99337329e-08, 2.05541344e-08, 2.09820854e-08, 2.12172608e-08,
             2.17732055e-08, 2.11404103e-08, 1.88170048e-08, 2.02018402e-08,
             1.85675297e-08, 1.67737362e-08, 1.47612083e-08, 1.39845033e-08,
             1.43860861e-08, 1.45533212e-08, 1.68728480e-08, 2.18248424e-08,
             2.00118091e-08, 2.62560054e-08, 3.27239533e-08, 4.31971259e-08,
             5.48310979e-08, 7.83728638e-08, 8.56392361e-08, 7.29909928e-08,
             7.89940344e-08, 7.16221535e-08, 8.24730719e-08, 8.97363108e-08,
             1.14854465e-07, 1.16268268e-07, 8.87113742e-08, 6.96811924e-07,
             6.34010848e-06, 1.42570707e-05, 1.82244657e-05, 1.80941097e-05,
             1.87573423e-05, 2.04298794e-05, 2.28651788e-05, 2.33484861e-05,
             2.10828657e-05, 1.31928864e-05, 1.17865256e-05, 2.07541343e-05,
             4.46153081e-06,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • Kd_heat
      (zeuc)
      float32
      1.001e-06 1.001e-06 ... nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      standard_name :

      ocean_vertical_heat_diffusivity

      array([1.0006705e-06, 1.0006337e-06, 1.0006618e-06, 1.0006443e-06,
             1.0006701e-06, 1.0006614e-06, 1.0006444e-06, 1.0006701e-06,
             1.0006628e-06, 1.0106939e-06, 1.0401673e-06, 1.0006507e-06,
             1.1119579e-06, 1.2560798e-06, 1.4861683e-06, 1.6268899e-06,
             1.3251024e-06, 1.4133223e-06, 1.2183413e-06, 1.3440165e-06,
             1.3007792e-06, 2.1559247e-06, 1.7224424e-06, 1.4074643e-06,
             1.1677428e-06, 1.0357805e-06, 1.0051077e-06, 1.0014580e-06,
             1.0006426e-06, 1.0018749e-06, 1.1331898e-06, 4.9953865e-06,
             1.3441498e-04, 5.2427029e-04, 1.1975758e-03, 2.1821284e-03,
             4.7503118e-03, 8.6501129e-03, 1.4562671e-02, 1.8755380e-02,
             1.9549852e-02, 1.4596995e-02, 1.4510026e-02, 2.2565287e-02,
             6.9412617e-03,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • chi
      (zeuc)
      float32
      7.983e-10 8.5e-10 ... nan nan

      long_name :

      $χ$

      units :

      C^2/s

      array([7.98321798e-10, 8.50012727e-10, 8.84189610e-10, 9.11799580e-10,
             9.56660640e-10, 9.04769315e-10, 7.18309079e-10, 8.29037838e-10,
             7.05128622e-10, 5.75203885e-10, 4.42727882e-10, 4.47146736e-10,
             4.10015882e-10, 3.96392363e-10, 4.70237904e-10, 7.21766591e-10,
             7.88129451e-10, 1.20723875e-09, 2.13015827e-09, 3.33278893e-09,
             5.25600186e-09, 7.23560190e-09, 9.75383418e-09, 8.95890473e-09,
             1.27101201e-08, 1.15444925e-08, 1.67442575e-08, 2.00238528e-08,
             2.98242746e-08, 3.13590256e-08, 1.84597369e-08, 2.74945506e-07,
             1.06124980e-06, 1.65242091e-06, 1.08279119e-06, 7.10793756e-07,
             4.89256706e-07, 3.81183526e-07, 3.18817285e-07, 3.70346726e-07,
             4.42385783e-07, 5.16218051e-07, 4.75548546e-07, 3.79147195e-07,
             8.43883186e-07,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • eps
      (zeuc)
      float32
      9.259e-11 1.049e-10 ... nan nan

      long_name :

      $ε$

      units :

      W/kg

      array([9.25853075e-11, 1.04935380e-10, 1.02905920e-10, 1.32156480e-10,
             1.15078946e-10, 1.16655560e-10, 1.43847920e-10, 1.19792704e-10,
             1.41715001e-10, 1.54142199e-10, 2.68964850e-10, 6.02213612e-10,
             3.46677242e-10, 7.41039119e-10, 1.00023889e-09, 1.85596560e-09,
             2.51813637e-09, 3.90712618e-09, 5.12678300e-09, 7.66280817e-09,
             8.26953261e-09, 1.09633360e-08, 1.24252670e-08, 1.39698848e-08,
             1.54164272e-08, 1.55849254e-08, 1.44626435e-08, 1.37376457e-08,
             1.04168105e-08, 2.90004687e-09, 4.27402691e-09, 1.44846027e-07,
             1.99676307e-07, 3.04392813e-07, 2.78771381e-07, 3.50479127e-07,
             4.06664299e-07, 4.18821770e-07, 3.92315798e-07, 3.80271871e-07,
             4.16405783e-07, 4.76534012e-07, 4.59916748e-07, 2.96663188e-07,
             4.61132771e-07,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • S2
      (zeuc)
      float32
      2.532e-07 3.085e-07 ... nan nan

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([2.53197157e-07, 3.08510607e-07, 2.92727435e-07, 4.32506539e-07,
             3.44354930e-07, 3.59136124e-07, 5.08331141e-07, 3.83767656e-07,
             5.07877189e-07, 5.82732639e-07, 1.15930766e-06, 2.78240032e-06,
             1.53309384e-06, 3.44462319e-06, 4.66998154e-06, 8.77538332e-06,
             1.20398645e-05, 1.87341047e-05, 2.45930187e-05, 3.67302637e-05,
             3.95981842e-05, 5.22509799e-05, 5.93275399e-05, 6.70128939e-05,
             7.40271207e-05, 7.50657564e-05, 6.94298578e-05, 6.57120399e-05,
             4.91650717e-05, 1.24117068e-05, 1.86605121e-05, 3.53242329e-04,
             3.78591998e-04, 3.90216097e-04, 1.84304707e-04, 1.55016198e-04,
             1.40105636e-04, 1.20296543e-04, 9.45677166e-05, 8.90597657e-05,
             9.51775655e-05, 1.15308751e-04, 1.09259403e-04, 7.42343545e-05,
             1.64657977e-04,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • Rig_T
      (zeuc)
      float32
      215.0 202.1 206.4 ... nan nan nan

      long_name :

      $Ri^g_T$

      array([2.1503494e+02, 2.0214403e+02, 2.0636514e+02, 2.0012900e+02,
             1.8109155e+02, 2.0417029e+02, 1.3411453e+02, 1.9008124e+02,
             1.5265700e+02, 1.5746408e+02, 4.6662010e+01, 1.1054462e+01,
             2.8667643e+01, 8.2051058e+00, 5.2001052e+00, 3.1951575e+00,
             2.6567822e+00, 2.2889338e+00, 2.2457385e+00, 1.9907213e+00,
             2.3420768e+00, 2.0011907e+00, 1.9492234e+00, 1.8765500e+00,
             2.0379128e+00, 2.0180886e+00, 2.5594373e+00, 2.9622624e+00,
             5.6772432e+00, 3.1978878e+01, 1.7669277e+01, 1.5686954e+00,
             1.0714781e+00, 5.2440548e-01, 3.9277917e-01, 2.9803014e-01,
             2.4751794e-01, 2.4547334e-01, 3.0760974e-01, 3.3345494e-01,
             3.1290802e-01, 1.8050675e-01, 1.9237757e-01, 2.6489717e-01,
             1.3896098e-02,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

  • Attributes: (0)

  <xarray.DatasetView>
  Dimensions:        (zeuc: 50)
  Coordinates:
      xh             float64 -140.0
      yh             float64 0.0625
      yq             float64 -0.0625
    * zeuc           (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5
  Data variables:
      uo             (zeuc) float32 0.004048 0.01676 0.006131 ... nan nan nan
      vo             (zeuc) float32 0.001048 0.002618 -0.001801 ... nan nan nan
      ν              (zeuc) float32 0.0002048 0.0002048 0.0002048 ... nan nan nan
      thetao         (zeuc) float32 10.4 10.54 10.86 11.05 ... nan nan nan nan
      Tflx_dia_diff  (zeuc) float32 2.117e-08 2.133e-08 2.272e-08 ... nan nan nan
      Kd_heat        (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan
      chi            (zeuc) float32 9.004e-10 9.152e-10 1.04e-09 ... nan nan nan
      eps            (zeuc) float32 1.218e-10 1.312e-10 1.211e-10 ... nan nan nan
      S2             (zeuc) float32 3.838e-07 4.264e-07 3.615e-07 ... nan nan nan
      Rig_T          (zeuc) float32 177.0 140.2 155.8 140.4 ... nan nan nan nan

  baseline.kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • zeuc: 50
  • Coordinates: (4)
    • xh
      ()
      float64
      -140.0

      cartesian_axis :

      X

      domain_decomposition :

      [220 222 220 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [210 258 210 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      cartesian_axis :

      Y

      domain_decomposition :

      [209 257 209 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zeuc
      (zeuc)
      float64
      -297.5 -287.5 ... 182.5 192.5

      array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5,
             -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5,
             -117.5, -107.5,  -97.5,  -87.5,  -77.5,  -67.5,  -57.5,  -47.5,  -37.5,
              -27.5,  -17.5,   -7.5,    2.5,   12.5,   22.5,   32.5,   42.5,   52.5,
               62.5,   72.5,   82.5,   92.5,  102.5,  112.5,  122.5,  132.5,  142.5,
              152.5,  162.5,  172.5,  182.5,  192.5])

  • Data variables: (10)
    • uo
      (zeuc)
      float32
      0.004048 0.01676 ... nan nan

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      array([ 0.00404828,  0.01676483,  0.0061307 ,  0.00345092,  0.01299025,
              0.00860957,  0.00583755,  0.00975394,  0.01646413,  0.0206379 ,
              0.03018612,  0.03431475,  0.04300458,  0.06420571,  0.08346127,
              0.11615828,  0.14630534,  0.19210795,  0.23916784,  0.3171235 ,
              0.3691892 ,  0.44943303,  0.5751361 ,  0.6041893 ,  0.71150935,
              0.78162134,  0.87785274,  0.95903206,  1.0309594 ,  1.0698215 ,
              1.0845604 ,  1.0534542 ,  0.89380115,  0.6437762 ,  0.46004242,
              0.30624336,  0.16267328,  0.04375282, -0.04332588, -0.1130536 ,
             -0.17761762, -0.13251486,  0.0162907 ,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan],
            dtype=float32)

    • vo
      (zeuc)
      float32
      0.001048 0.002618 ... nan nan

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      array([ 0.00104795,  0.00261766, -0.0018014 ,  0.00037841,  0.0025488 ,
             -0.00187899, -0.00026791,  0.00100588,  0.00070265,  0.00101431,
              0.00384123, -0.00017991,  0.00037468,  0.00011675, -0.0017148 ,
             -0.00150565, -0.00363744, -0.00322482, -0.0050785 , -0.00597607,
             -0.0056937 , -0.00600529, -0.0086971 , -0.00675561, -0.00828242,
             -0.00761547, -0.00865436, -0.00872748, -0.00841743, -0.00749545,
             -0.00702322, -0.00871168, -0.01047203, -0.01597641, -0.02200845,
             -0.02644102, -0.02778417, -0.02557627, -0.02107927, -0.01428133,
             -0.00863254,  0.00180638,  0.00251294,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan],
            dtype=float32)

    • ν
      (zeuc)
      float32
      0.0002048 0.0002048 ... nan nan

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([0.00020475, 0.00020475, 0.00020479, 0.00020481, 0.00020481,
             0.00020482, 0.00020483, 0.00020483, 0.00020483, 0.00020485,
             0.00020483, 0.00020489, 0.00020488, 0.00020487, 0.00020488,
             0.00020491, 0.00020493, 0.00020495, 0.00020495, 0.00020496,
             0.00020496, 0.00020498, 0.000205  , 0.00020501, 0.00020503,
             0.00020506, 0.0002051 , 0.00020513, 0.00020516, 0.00020519,
             0.00020521, 0.00022332, 0.00031059, 0.00066837, 0.00124214,
             0.00244679, 0.00480632, 0.00820587, 0.00956165, 0.01016004,
             0.00855367, 0.00905411, 0.00920703,        nan,        nan,
                    nan,        nan,        nan,        nan,        nan],
            dtype=float32)

    • thetao
      (zeuc)
      float32
      10.4 10.54 10.86 ... nan nan nan

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([10.40172  , 10.537823 , 10.859438 , 11.050811 , 11.223505 ,
             11.51517  , 11.68402  , 11.896485 , 12.074736 , 12.329481 ,
             12.410086 , 12.484327 , 12.630804 , 12.72751  , 12.877808 ,
             12.997511 , 13.182478 , 13.378202 , 13.6936865, 14.069012 ,
             14.489995 , 15.146014 , 15.748816 , 16.187355 , 17.096003 ,
             17.545921 , 18.504902 , 19.545824 , 20.847746 , 21.178389 ,
             22.544098 , 23.383211 , 24.294075 , 25.223984 , 25.63944  ,
             25.85893  , 26.019669 , 26.134165 , 26.18872  , 26.17477  ,
             26.322565 , 26.62483  , 26.912878 ,        nan,        nan,
                    nan,        nan,        nan,        nan,        nan],
            dtype=float32)

    • Tflx_dia_diff
      (zeuc)
      float32
      2.117e-08 2.133e-08 ... nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      standard_name :

      ocean_vertical_diffusive_heat_flux

      array([2.11659774e-08, 2.13343228e-08, 2.27206094e-08, 2.12139071e-08,
             2.17982397e-08, 2.17070539e-08, 1.85763174e-08, 1.89413871e-08,
             1.71070216e-08, 1.51437192e-08, 1.35404239e-08, 1.59176636e-08,
             1.31053639e-08, 1.83535800e-08, 2.00722408e-08, 1.85805007e-08,
             2.17158664e-08, 2.99371763e-08, 3.47126132e-08, 4.60725538e-08,
             5.43710925e-08, 6.53851089e-08, 6.50946816e-08, 6.93698965e-08,
             7.52715721e-08, 8.42500185e-08, 8.77352164e-08, 9.17952292e-08,
             1.13108079e-07, 1.20545963e-07, 8.29441831e-08, 1.44966236e-07,
             6.83658254e-06, 1.46440325e-05, 1.75006007e-05, 1.90046012e-05,
             2.07540070e-05, 2.20161055e-05, 2.00347113e-05, 1.61481894e-05,
             1.27846570e-05, 8.64653157e-06, 1.04316132e-05,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • Kd_heat
      (zeuc)
      float32
      1.001e-06 1.001e-06 ... nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([1.0006451e-06, 1.0006506e-06, 1.0006515e-06, 1.0006414e-06,
             1.0006618e-06, 1.0006513e-06, 1.0006434e-06, 1.0006655e-06,
             1.0176084e-06, 1.0149389e-06, 1.0019072e-06, 1.2137010e-06,
             1.0805032e-06, 1.4297108e-06, 1.4507829e-06, 1.1778233e-06,
             1.1204596e-06, 1.1991365e-06, 1.0945305e-06, 1.0956771e-06,
             1.1185289e-06, 1.2680930e-06, 1.1178845e-06, 1.0855989e-06,
             1.0786908e-06, 1.0059720e-06, 1.0019743e-06, 1.0011390e-06,
             1.0006572e-06, 1.0011006e-06, 1.1255088e-06, 1.8804314e-05,
             1.1712696e-04, 4.8406073e-04, 1.1037614e-03, 3.1598986e-03,
             6.9173714e-03, 1.1358041e-02, 1.3600050e-02, 1.3077446e-02,
             1.2642518e-02, 1.0288579e-02, 1.1792418e-02,           nan,
                       nan,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • chi
      (zeuc)
      float32
      9.004e-10 9.152e-10 ... nan nan

      long_name :

      $χ$

      units :

      C^2/s

      array([9.0043989e-10, 9.1522040e-10, 1.0396450e-09, 9.1001395e-10,
             9.6060004e-10, 9.5469777e-10, 7.0223621e-10, 7.3680501e-10,
             5.9830035e-10, 4.7491955e-10, 3.9812650e-10, 4.7236026e-10,
             3.7199399e-10, 5.7536242e-10, 6.8126182e-10, 7.5827411e-10,
             1.0768707e-09, 1.8212872e-09, 2.5872873e-09, 4.2573043e-09,
             5.7363847e-09, 7.2878090e-09, 8.2911358e-09, 9.9289998e-09,
             1.2009678e-08, 1.5639399e-08, 1.8246034e-08, 1.9992989e-08,
             2.9070740e-08, 3.2245666e-08, 1.5850427e-08, 3.6572640e-08,
             1.2725955e-06, 1.6085360e-06, 1.2024104e-06, 7.9952855e-07,
             5.2454573e-07, 4.2225017e-07, 4.9829407e-07, 5.8574591e-07,
             4.9689334e-07, 5.1183719e-07, 4.9258375e-07,           nan,
                       nan,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • eps
      (zeuc)
      float32
      1.218e-10 1.312e-10 ... nan nan

      long_name :

      $ε$

      units :

      W/kg

      array([1.2180949e-10, 1.3122294e-10, 1.2108119e-10, 1.6528152e-10,
             1.4035438e-10, 1.1820606e-10, 1.9289763e-10, 1.5263664e-10,
             1.8540991e-10, 2.3599367e-10, 2.0692258e-10, 7.2218748e-10,
             6.4210226e-10, 1.0354551e-09, 1.6377171e-09, 2.4149363e-09,
             3.4051888e-09, 4.9914552e-09, 6.9494068e-09, 9.0220658e-09,
             1.0754879e-08, 1.2555609e-08, 1.3969577e-08, 1.5608988e-08,
             1.6270414e-08, 1.6123863e-08, 1.6080397e-08, 1.3522453e-08,
             9.2339345e-09, 2.6684295e-09, 3.6976919e-09, 9.5134418e-08,
             2.9680785e-07, 3.5708706e-07, 4.7353410e-07, 5.1698737e-07,
             5.3716803e-07, 5.2572875e-07, 5.2556476e-07, 5.5077942e-07,
             5.1904334e-07, 4.9139135e-07, 5.1879834e-07,           nan,
                       nan,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • S2
      (zeuc)
      float32
      3.838e-07 4.264e-07 ... nan nan

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([3.8381995e-07, 4.2644211e-07, 3.6150070e-07, 5.9201142e-07,
             4.6376638e-07, 3.5570309e-07, 7.5283435e-07, 5.5199490e-07,
             7.2743495e-07, 9.9200213e-07, 8.6687038e-07, 3.3345341e-06,
             2.9840382e-06, 4.8193274e-06, 7.7370651e-06, 1.1557462e-05,
             1.6346772e-05, 2.3976709e-05, 3.3468012e-05, 4.3415948e-05,
             5.1736562e-05, 6.0332572e-05, 6.7232111e-05, 7.5159194e-05,
             7.8313511e-05, 7.7508535e-05, 7.7205652e-05, 6.4623826e-05,
             4.3322329e-05, 1.1143856e-05, 1.5413378e-05, 2.9862832e-04,
             6.0351175e-04, 4.6424806e-04, 3.6171466e-04, 2.8594036e-04,
             2.1869803e-04, 1.7350054e-04, 1.7688426e-04, 1.9089431e-04,
             1.7569039e-04, 1.9307368e-04, 1.8426294e-04,           nan,
                       nan,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • Rig_T
      (zeuc)
      float32
      177.0 140.2 155.8 ... nan nan nan

      long_name :

      $Ri^g_T$

      array([1.7700241e+02, 1.4017377e+02, 1.5582840e+02, 1.4044011e+02,
             1.5148082e+02, 1.7303912e+02, 1.1180737e+02, 1.4587625e+02,
             1.2738346e+02, 6.4905960e+01, 4.9831394e+01, 8.3011351e+00,
             1.0125983e+01, 5.7563944e+00, 3.7067041e+00, 2.7272551e+00,
             2.4012811e+00, 2.1475210e+00, 1.9123729e+00, 1.9800160e+00,
             2.0326688e+00, 1.8785818e+00, 1.8993816e+00, 1.8516837e+00,
             1.9699457e+00, 2.4552357e+00, 2.5303655e+00, 3.2691784e+00,
             6.8624544e+00, 3.8340775e+01, 2.2407738e+01, 1.0736854e+00,
             4.4377226e-01, 3.1085035e-01, 2.2195560e-01, 1.8700995e-01,
             1.9031787e-01, 2.2211176e-01, 2.2589630e-01, 1.9110769e-01,
             1.7184874e-01, 1.3849184e-01, 2.1321297e-01,           nan,
                       nan,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

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  Dimensions:        (zeuc: 50)
  Coordinates:
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      yh             float64 0.0625
      yq             float64 -0.0625
    * zeuc           (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5
  Data variables:
      uo             (zeuc) float32 0.02499 -0.01359 0.01056 ... nan nan nan
      vo             (zeuc) float32 0.001215 -0.0004066 0.0001778 ... nan nan nan
      ν              (zeuc) float32 5.046e-06 5.048e-06 5.042e-06 ... nan nan nan
      thetao         (zeuc) float32 9.926 10.25 10.35 10.54 ... nan nan nan nan
      Tflx_dia_diff  (zeuc) float32 1.755e-08 1.924e-08 1.924e-08 ... nan nan nan
      Kd_heat        (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan
      chi            (zeuc) float32 6.202e-10 7.44e-10 7.452e-10 ... nan nan nan
      eps            (zeuc) float32 3.79e-11 4.269e-11 4.291e-11 ... nan nan nan
      S2             (zeuc) float32 2.995e-07 2.884e-07 3.947e-07 ... nan nan nan
      Rig_T          (zeuc) float32 180.5 238.2 184.8 129.5 ... nan nan nan nan

  new_baseline.hb

  • Groups: (0)
  • Dimensions:
    • zeuc: 50
  • Coordinates: (4)
    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220 222 220 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210 258 210 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209 257 209 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zeuc
      (zeuc)
      float64
      -297.5 -287.5 ... 182.5 192.5

      array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5,
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  • Data variables: (10)
    • uo
      (zeuc)
      float32
      0.02499 -0.01359 ... nan nan

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      array([ 0.02499344, -0.01359064,  0.01056438,  0.02405857, -0.01237831,
              0.01013298,  0.02191616, -0.00767961,  0.00422038,  0.00949787,
              0.0014572 ,  0.00401488,  0.01101161,  0.00240373,  0.01586246,
              0.00856926,  0.03002909,  0.03372687,  0.07525551,  0.11013176,
              0.18264885,  0.2575284 ,  0.32371256,  0.41555193,  0.50246024,
              0.6455064 ,  0.75232065,  0.91769034,  1.0688589 ,  1.1614565 ,
              1.1729106 ,  1.0739865 ,  0.78872025,  0.62034184,  0.49858826,
              0.380375  ,  0.27201495,  0.16802388,  0.07608254,  0.00831099,
             -0.04175798, -0.08453508, -0.04369244,  0.12923996,  0.08277461,
                     nan,         nan,         nan,         nan,         nan],
            dtype=float32)

    • vo
      (zeuc)
      float32
      0.001215 -0.0004066 ... nan nan

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      array([ 0.00121472, -0.00040661,  0.00017783,  0.00129372, -0.00017322,
              0.00046198,  0.00148334, -0.00020848, -0.00060944,  0.00136364,
             -0.00039485, -0.00453154,  0.00167347, -0.00063127,  0.00158471,
             -0.00220249, -0.00043014, -0.00388327, -0.0008026 , -0.00243983,
              0.0007454 ,  0.00054837, -0.00046543, -0.0001288 , -0.00214814,
             -0.00606339, -0.00876785, -0.00452488, -0.0027157 , -0.00483246,
             -0.01359488, -0.00209842,  0.00908873,  0.0029544 , -0.00833544,
             -0.01829557, -0.02495088, -0.02942124, -0.03080559, -0.02925389,
             -0.02618347, -0.02168874, -0.0189103 , -0.06369759, -0.05137211,
                     nan,         nan,         nan,         nan,         nan],
            dtype=float32)

    • ν
      (zeuc)
      float32
      5.046e-06 5.048e-06 ... nan nan

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([5.0462340e-06, 5.0475892e-06, 5.0424624e-06, 5.0440090e-06,
             5.0467970e-06, 5.0418971e-06, 5.0431631e-06, 5.0451308e-06,
             5.0414542e-06, 5.0420358e-06, 5.0389685e-06, 5.0458352e-06,
             5.0413701e-06, 5.0399844e-06, 5.0401809e-06, 5.0381868e-06,
             5.1015418e-06, 6.8741460e-06, 1.5321362e-05, 3.2997450e-05,
             5.5269622e-05, 8.3268496e-05, 6.9354384e-05, 9.5276482e-05,
             7.8466102e-05, 4.6640664e-05, 3.3242406e-05, 1.1284424e-05,
             5.3480094e-06, 1.8180930e-05, 8.6516111e-06, 7.5917036e-05,
             4.6185430e-04, 9.8257314e-04, 1.4048299e-03, 2.0107760e-03,
             3.2659161e-03, 5.8874162e-03, 9.1227721e-03, 1.1989832e-02,
             1.2402624e-02, 1.1848887e-02, 1.1853820e-02, 1.4631215e-02,
             3.4738344e-03,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • thetao
      (zeuc)
      float32
      9.926 10.25 10.35 ... nan nan nan

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([ 9.925618 , 10.254531 , 10.354003 , 10.53566  , 10.865824 ,
             10.970225 , 11.218256 , 11.542037 , 11.700399 , 12.0454   ,
             12.268132 , 12.709218 , 12.647938 , 12.898456 , 13.133925 ,
             13.318799 , 13.4945545, 13.652494 , 13.850175 , 14.109336 ,
             14.381747 , 14.7182045, 15.119492 , 15.392733 , 15.855298 ,
             16.483904 , 17.017002 , 18.144295 , 19.60642  , 20.326212 ,
             20.99324  , 22.676971 , 24.507181 , 25.129498 , 25.416185 ,
             25.618725 , 25.778563 , 25.90913  , 26.007828 , 26.056711 ,
             26.065672 , 26.052399 , 26.221981 , 26.439838 , 26.489485 ,
                    nan,        nan,        nan,        nan,        nan],
            dtype=float32)

    • Tflx_dia_diff
      (zeuc)
      float32
      1.755e-08 1.924e-08 ... nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      array([1.7554951e-08, 1.9238920e-08, 1.9244153e-08, 2.2659853e-08,
             2.0953284e-08, 2.2788708e-08, 2.6120119e-08, 2.4164978e-08,
             2.5351335e-08, 2.5231577e-08, 2.4755963e-08, 2.2278645e-08,
             2.3270013e-08, 2.1283897e-08, 1.9231832e-08, 1.8315641e-08,
             1.7733992e-08, 2.5324256e-08, 8.0707700e-08, 3.8407686e-07,
             7.6808874e-07, 1.3642584e-06, 2.3333414e-06, 2.6994053e-06,
             2.2198003e-06, 1.8574679e-06, 7.7228543e-07, 5.3184516e-07,
             2.6284050e-07, 1.1193101e-07, 2.0913073e-07, 1.4701823e-05,
             1.4408063e-05, 2.3515480e-05, 2.2960936e-05, 2.2215781e-05,
             2.2256960e-05, 2.3240704e-05, 2.4761674e-05, 2.5259005e-05,
             2.2617920e-05, 1.5300162e-05, 1.2066351e-05, 2.2057413e-05,
             2.8274803e-06,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • Kd_heat
      (zeuc)
      float32
      1.001e-06 1.001e-06 ... nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([1.00069019e-06, 1.00065347e-06, 1.00067587e-06, 1.00063551e-06,
             1.00067132e-06, 1.00066575e-06, 1.00063346e-06, 1.00066700e-06,
             1.00066563e-06, 1.00066734e-06, 1.00066416e-06, 1.00062528e-06,
             1.00066745e-06, 1.00067314e-06, 1.00482680e-06, 1.07401365e-06,
             1.08523659e-06, 1.39880501e-06, 4.77538333e-06, 2.90892385e-05,
             4.31792432e-05, 6.99554439e-05, 9.46688160e-05, 9.24607375e-05,
             7.00155651e-05, 5.26795411e-05, 1.31007955e-05, 5.59393993e-06,
             2.53372355e-06, 1.77613265e-06, 1.33658182e-06, 2.10412065e-04,
             3.71373346e-04, 8.84195731e-04, 1.33982731e-03, 2.16023228e-03,
             4.38748859e-03, 7.71976588e-03, 1.28700137e-02, 1.76899210e-02,
             1.95113998e-02, 1.61320884e-02, 1.55900959e-02, 2.49941684e-02,
             7.19807623e-03,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • chi
      (zeuc)
      float32
      6.202e-10 7.44e-10 ... nan nan

      long_name :

      $χ$

      units :

      C^2/s

      array([6.2018657e-10, 7.4400919e-10, 7.4519668e-10, 1.0408558e-09,
             8.8766766e-10, 1.0477264e-09, 1.3862269e-09, 1.1847184e-09,
             1.3015795e-09, 1.2918184e-09, 1.2502051e-09, 1.0106910e-09,
             1.1095831e-09, 9.3900832e-10, 7.7561230e-10, 6.9214173e-10,
             6.8843903e-10, 1.1982592e-09, 3.6828227e-09, 1.5802890e-08,
             3.6510770e-08, 7.2088909e-08, 1.4925237e-07, 2.0199380e-07,
             1.9517613e-07, 1.8122286e-07, 1.1386960e-07, 1.1739417e-07,
             6.3101886e-08, 2.3677337e-08, 7.7539383e-08, 2.8464769e-06,
             1.7457008e-06, 1.9661063e-06, 1.1580179e-06, 8.1946200e-07,
             5.9891721e-07, 4.6636660e-07, 3.9778465e-07, 3.8270457e-07,
             4.3542067e-07, 4.8403251e-07, 4.3925988e-07, 4.1374929e-07,
             6.2473862e-07,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • eps
      (zeuc)
      float32
      3.79e-11 4.269e-11 ... nan nan

      long_name :

      $ε$

      units :

      W/kg

      array([3.79035345e-11, 4.26914233e-11, 4.29057762e-11, 5.24651260e-11,
             4.79460464e-11, 5.31061480e-11, 6.14722440e-11, 5.75307059e-11,
             6.05433897e-11, 6.09162165e-11, 5.84663429e-11, 5.39510173e-11,
             5.40397310e-11, 5.01869067e-11, 4.56859550e-11, 5.23621077e-11,
             1.11069591e-10, 5.57708157e-10, 1.55664848e-09, 3.27052985e-09,
             5.70149794e-09, 8.47223358e-09, 1.33512481e-08, 1.50298956e-08,
             1.40175995e-08, 1.27147386e-08, 7.99094746e-09, 6.56495258e-09,
             3.95846644e-09, 6.83437806e-10, 3.68607829e-08, 3.28708552e-07,
             2.60447365e-07, 2.61275943e-07, 3.00201549e-07, 3.76866780e-07,
             4.21417553e-07, 4.29890179e-07, 4.11924589e-07, 3.76672062e-07,
             3.74530913e-07, 4.16727289e-07, 4.40827421e-07, 3.00241652e-07,
             5.02966031e-07,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • S2
      (zeuc)
      float32
      2.995e-07 2.884e-07 ... nan nan

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([2.99454200e-07, 2.88390282e-07, 3.94741505e-07, 5.30968293e-07,
             4.65758774e-07, 5.12829388e-07, 5.89175841e-07, 6.17585215e-07,
             6.72956560e-07, 8.13043584e-07, 7.72349836e-07, 6.12035194e-07,
             6.96600068e-07, 7.94913888e-07, 7.87037322e-07, 1.80515713e-06,
             4.14777469e-06, 1.13599381e-05, 2.49655386e-05, 3.86873107e-05,
             5.25923242e-05, 6.51414230e-05, 8.23767332e-05, 1.08375476e-04,
             1.27818377e-04, 1.52830122e-04, 1.93246451e-04, 2.14157830e-04,
             2.23237090e-04, 5.20113535e-05, 1.84496806e-04, 8.95032368e-04,
             6.05278881e-04, 2.61586159e-04, 1.78649512e-04, 1.69708088e-04,
             1.52827561e-04, 1.26139494e-04, 1.06460036e-04, 9.21873580e-05,
             9.53566050e-05, 1.12116184e-04, 1.05927014e-04, 7.23837584e-05,
             1.64326877e-04,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • Rig_T
      (zeuc)
      float32
      180.5 238.2 184.8 ... nan nan nan

      long_name :

      $Ri^g_T$

      array([1.80487656e+02, 2.38210449e+02, 1.84815262e+02, 1.29452072e+02,
             1.42724121e+02, 1.33232925e+02, 1.08492073e+02, 1.15649536e+02,
             1.18179962e+02, 9.78238068e+01, 1.05259544e+02, 1.15983322e+02,
             1.20559654e+02, 1.13338959e+02, 1.05069458e+02, 6.24187737e+01,
             2.53599625e+01, 1.01569824e+01, 3.78092313e+00, 1.77504945e+00,
             1.37038183e+00, 1.04456234e+00, 9.38269973e-01, 8.45975637e-01,
             7.97548473e-01, 7.48805523e-01, 9.33551669e-01, 1.23177063e+00,
             1.45686054e+00, 6.68316460e+00, 8.43145561e+00, 5.06967545e-01,
             5.58340728e-01, 4.84673023e-01, 3.70436102e-01, 3.00139725e-01,
             2.60080546e-01, 2.52664149e-01, 3.00420284e-01, 3.45842630e-01,
             3.32556754e-01, 2.14251995e-01, 1.95444688e-01, 3.57639253e-01,
             3.24461162e-02,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

  • Attributes: (0)

  <xarray.DatasetView>
  Dimensions:        (zeuc: 50)
  Coordinates:
      xh             float64 -140.0
      yh             float64 0.0625
      yq             float64 -0.0625
    * zeuc           (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5
  Data variables:
      uo             (zeuc) float32 -0.01065 0.02048 0.01105 ... nan nan nan
      vo             (zeuc) float32 -0.0004901 0.0005714 -0.001145 ... nan nan nan
      ν              (zeuc) float32 5.046e-06 5.043e-06 5.045e-06 ... nan nan nan
      thetao         (zeuc) float32 10.29 10.42 10.62 10.87 ... nan nan nan nan
      Tflx_dia_diff  (zeuc) float32 1.85e-08 1.913e-08 2.062e-08 ... nan nan nan
      Kd_heat        (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan
      chi            (zeuc) float32 6.88e-10 7.366e-10 8.582e-10 ... nan nan nan
      eps            (zeuc) float32 4.024e-11 4.357e-11 4.642e-11 ... nan nan nan
      S2             (zeuc) float32 3.245e-07 5.718e-07 4.315e-07 ... nan nan nan
      Rig_T          (zeuc) float32 186.3 128.5 132.1 119.8 ... nan nan nan nan

  new_baseline.kpp.lmd.004

  • Groups: (0)
  • Dimensions:
    • zeuc: 50
  • Coordinates: (4)
    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220 222 220 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210 258 210 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209 257 209 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zeuc
      (zeuc)
      float64
      -297.5 -287.5 ... 182.5 192.5

      array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5,
             -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5,
             -117.5, -107.5,  -97.5,  -87.5,  -77.5,  -67.5,  -57.5,  -47.5,  -37.5,
              -27.5,  -17.5,   -7.5,    2.5,   12.5,   22.5,   32.5,   42.5,   52.5,
               62.5,   72.5,   82.5,   92.5,  102.5,  112.5,  122.5,  132.5,  142.5,
              152.5,  162.5,  172.5,  182.5,  192.5])

  • Data variables: (10)
    • uo
      (zeuc)
      float32
      -0.01065 0.02048 ... nan nan

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      array([-1.0646891e-02,  2.0477358e-02,  1.1050474e-02, -8.4186951e-03,
              1.7257325e-02,  1.1625963e-02, -6.5456191e-03,  1.3428366e-02,
             -4.1635828e-03,  8.3168782e-03, -3.1610057e-04, -4.4213003e-03,
              4.7586383e-03, -1.6103460e-03,  5.9380159e-03,  1.0472262e-02,
              2.0668298e-02,  4.8041590e-02,  7.7255875e-02,  1.3745204e-01,
              1.9449836e-01,  2.8766719e-01,  4.3414849e-01,  5.1080972e-01,
              6.4297825e-01,  7.6984107e-01,  9.1899163e-01,  1.0664825e+00,
              1.1405255e+00,  1.2641385e+00,  1.2902447e+00,  1.2021922e+00,
              9.1631228e-01,  6.8355072e-01,  5.1118356e-01,  3.5848483e-01,
              2.2015458e-01,  1.0378160e-01,  1.9374512e-02, -3.7018765e-02,
             -9.4496332e-02, -4.8449118e-02, -4.7313720e-02,  1.0204794e-01,
              5.5657301e-02,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • vo
      (zeuc)
      float32
      -0.0004901 0.0005714 ... nan nan

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      array([-0.00049009,  0.00057143, -0.00114546, -0.00014106,  0.00080116,
             -0.00064699, -0.00018529,  0.00069077,  0.00067059, -0.0008774 ,
              0.00098835, -0.00173778, -0.00020317, -0.00044024,  0.00021668,
             -0.0002896 , -0.00128701, -0.00099582, -0.00125251, -0.00213526,
             -0.00148937, -0.0002665 , -0.00644217, -0.0011105 , -0.00474673,
             -0.00347817, -0.0042568 , -0.0022129 , -0.00164185, -0.00106187,
             -0.00665514, -0.0052943 , -0.00831086, -0.01532346, -0.02331558,
             -0.02841561, -0.02930233, -0.0267921 , -0.02194007, -0.01483669,
             -0.00246736, -0.00684613, -0.0120682 ,  0.09584202,  0.11942156,
                     nan,         nan,         nan,         nan,         nan],
            dtype=float32)

    • ν
      (zeuc)
      float32
      5.046e-06 5.043e-06 ... nan nan

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([5.0464682e-06, 5.0427479e-06, 5.0452982e-06, 5.0455860e-06,
             5.0420449e-06, 5.0442563e-06, 5.0445096e-06, 5.0414201e-06,
             5.0418244e-06, 5.0409303e-06, 5.0384342e-06, 5.0429262e-06,
             5.0398326e-06, 5.0403924e-06, 5.0384015e-06, 5.0397739e-06,
             5.0431672e-06, 5.1221195e-06, 1.2207116e-05, 1.7747578e-05,
             1.4220629e-05, 1.1861085e-05, 1.7739267e-05, 1.5002135e-05,
             1.2204411e-05, 1.0666293e-05, 6.1089358e-06, 5.0663079e-06,
             5.0405142e-06, 2.5051277e-05, 2.0913076e-05, 5.0343264e-05,
             3.2911345e-04, 7.6059770e-04, 1.3257149e-03, 2.7636024e-03,
             5.2530593e-03, 7.9990737e-03, 9.9148713e-03, 1.0457821e-02,
             8.8949250e-03, 9.7109433e-03, 9.9065546e-03, 1.7521054e-02,
             3.3595981e-03,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • thetao
      (zeuc)
      float32
      10.29 10.42 10.62 ... nan nan nan

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([10.287295 , 10.417949 , 10.615116 , 10.869449 , 11.04266  ,
             11.262654 , 11.567776 , 11.807474 , 12.077805 , 12.501555 ,
             12.587448 , 12.865992 , 13.096592 , 13.335708 , 13.5657015,
             13.746414 , 13.889223 , 14.036531 , 14.221035 , 14.368961 ,
             14.648155 , 15.087256 , 15.539373 , 15.975408 , 16.745865 ,
             17.152761 , 18.009958 , 19.28983  , 20.677986 , 21.19363  ,
             22.335844 , 23.474924 , 24.749414 , 25.383026 , 25.68066  ,
             25.866386 , 26.00263  , 26.103117 , 26.162252 , 26.248213 ,
             26.393143 , 26.920116 , 27.855074 , 26.945793 , 26.995775 ,
                    nan,        nan,        nan,        nan,        nan],
            dtype=float32)

    • Tflx_dia_diff
      (zeuc)
      float32
      1.85e-08 1.913e-08 ... nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      array([1.85005504e-08, 1.91293861e-08, 2.06160440e-08, 2.21359180e-08,
             2.24070380e-08, 2.48522607e-08, 2.56552166e-08, 2.63386593e-08,
             2.74371743e-08, 2.67701328e-08, 2.69359877e-08, 2.44756340e-08,
             2.41357050e-08, 2.14560352e-08, 1.97716332e-08, 1.66414509e-08,
             1.74895423e-08, 2.14079350e-08, 3.45732083e-08, 6.12063857e-08,
             7.47684510e-08, 1.62360351e-07, 2.77199149e-07, 4.46485075e-07,
             3.56709933e-07, 1.23152688e-07, 1.46308878e-07, 1.06570774e-07,
             1.38309090e-07, 1.07144494e-07, 1.11251758e-07, 5.28505052e-06,
             1.73781937e-05, 1.98480011e-05, 2.11431343e-05, 2.20121365e-05,
             2.35071038e-05, 2.38630219e-05, 2.23098959e-05, 1.71911925e-05,
             1.30739172e-05, 8.85367081e-06, 9.64845640e-06, 2.54260249e-05,
             3.50545884e-06,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • Kd_heat
      (zeuc)
      float32
      1.001e-06 1.001e-06 ... nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([1.0006723e-06, 1.0006611e-06, 1.0006628e-06, 1.0006404e-06,
             1.0006654e-06, 1.0006577e-06, 1.0006396e-06, 1.0006671e-06,
             1.0006659e-06, 1.0006629e-06, 1.0006570e-06, 1.0006389e-06,
             1.0006675e-06, 1.0019558e-06, 1.0135477e-06, 1.0423036e-06,
             1.2229641e-06, 1.4524945e-06, 1.7310816e-05, 1.5045007e-05,
             3.8584258e-06, 6.2752624e-06, 8.8590523e-06, 1.3132055e-05,
             1.0210648e-05, 2.2401643e-06, 3.0851486e-06, 1.0444164e-06,
             1.0006403e-06, 6.2561652e-05, 1.0642791e-06, 7.5733333e-05,
             4.5665741e-04, 7.8447431e-04, 1.5393369e-03, 3.8610261e-03,
             8.3111003e-03, 1.1603486e-02, 1.4628670e-02, 1.4060708e-02,
             1.3308118e-02, 1.1162918e-02, 1.2060479e-02, 2.7877459e-02,
             5.9886063e-03,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • chi
      (zeuc)
      float32
      6.88e-10 7.366e-10 ... nan nan

      long_name :

      $χ$

      units :

      C^2/s

      array([6.8802869e-10, 7.3664508e-10, 8.5824386e-10, 9.8956543e-10,
             1.0133299e-09, 1.2501800e-09, 1.3284884e-09, 1.4034628e-09,
             1.5236361e-09, 1.4596947e-09, 1.4793824e-09, 1.2308681e-09,
             1.2104571e-09, 9.6535835e-10, 8.2865687e-10, 5.9357264e-10,
             5.8739025e-10, 8.6316454e-10, 1.1760135e-09, 2.8925231e-09,
             4.5060946e-09, 1.1985256e-08, 2.2661380e-08, 4.0463782e-08,
             3.4963129e-08, 1.8348556e-08, 2.2507315e-08, 2.5304370e-08,
             4.1292687e-08, 2.5772588e-08, 2.9690797e-08, 1.0108992e-06,
             2.0222787e-06, 1.5631721e-06, 1.1606248e-06, 8.0264130e-07,
             5.3737216e-07, 4.3478525e-07, 4.4919923e-07, 5.4489811e-07,
             4.6658363e-07, 4.9533986e-07, 3.4559400e-07, 3.4655343e-07,
             4.7845720e-07,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • eps
      (zeuc)
      float32
      4.024e-11 4.357e-11 ... nan nan

      long_name :

      $ε$

      units :

      W/kg

      array([4.0239829e-11, 4.3565509e-11, 4.6415576e-11, 5.2155967e-11,
             5.3069691e-11, 5.9277278e-11, 6.3031767e-11, 6.5039168e-11,
             6.8138634e-11, 6.6525035e-11, 6.4967774e-11, 5.9018707e-11,
             5.7870639e-11, 5.0631468e-11, 5.2535771e-11, 4.8821273e-11,
             8.2792731e-11, 2.4017247e-10, 3.2558109e-10, 5.9656929e-10,
             9.9171138e-10, 2.1262123e-09, 2.7699996e-09, 3.7947077e-09,
             3.7461754e-09, 1.7915618e-09, 1.9632773e-09, 1.3069008e-09,
             2.7872606e-09, 5.1370547e-10, 2.4214209e-08, 2.9136123e-07,
             2.7370675e-07, 3.9694919e-07, 4.9394691e-07, 5.2860804e-07,
             5.0609714e-07, 4.7452161e-07, 4.8870527e-07, 5.5493587e-07,
             5.5155920e-07, 5.5100037e-07, 6.9890541e-07, 1.1058072e-06,
             2.2871466e-06,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • S2
      (zeuc)
      float32
      3.245e-07 5.718e-07 ... nan nan

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([3.2451570e-07, 5.7184326e-07, 4.3148790e-07, 6.0657896e-07,
             6.7605208e-07, 5.9352175e-07, 8.2924151e-07, 8.5900632e-07,
             8.6205756e-07, 1.0500863e-06, 8.3261909e-07, 7.6358799e-07,
             7.8491541e-07, 7.2332011e-07, 1.5663835e-06, 2.1174208e-06,
             7.1297836e-06, 1.3430081e-05, 2.4580020e-05, 4.0981133e-05,
             6.2299107e-05, 9.6839991e-05, 1.3706491e-04, 1.7594828e-04,
             1.9717270e-04, 1.9812472e-04, 1.8732122e-04, 1.7612877e-04,
             1.7498141e-04, 4.2245716e-05, 1.6423324e-04, 1.1454133e-03,
             5.2541826e-04, 4.1192962e-04, 3.3999418e-04, 2.7700741e-04,
             2.1282340e-04, 1.6937664e-04, 1.5941910e-04, 1.7767286e-04,
             1.6364509e-04, 1.8765387e-04, 1.9949353e-04, 1.1106770e-04,
             1.6636330e-04,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • Rig_T
      (zeuc)
      float32
      186.3 128.5 132.1 ... nan nan nan

      long_name :

      $Ri^g_T$

      array([1.8634216e+02, 1.2853084e+02, 1.3210474e+02, 1.1979617e+02,
             1.0202711e+02, 1.0279958e+02, 9.7481445e+01, 8.4427910e+01,
             9.3046837e+01, 8.0721817e+01, 1.0160206e+02, 9.5520500e+01,
             9.3262207e+01, 1.0652971e+02, 5.7025261e+01, 4.3027679e+01,
             1.1232176e+01, 4.4198289e+00, 1.9743326e+00, 1.4598441e+00,
             1.1865945e+00, 9.0280277e-01, 7.5970727e-01, 6.7375970e-01,
             6.7109162e-01, 8.1848937e-01, 1.0103055e+00, 1.4284228e+00,
             2.1402125e+00, 1.0676737e+01, 7.4439979e+00, 3.3229971e-01,
             3.1535965e-01, 2.4582326e-01, 1.9892570e-01, 1.7789739e-01,
             1.8457843e-01, 2.1493007e-01, 2.4419188e-01, 1.9702415e-01,
             1.6759664e-01, 1.4394848e-01, 1.8335819e-01, 3.7373954e-01,
             5.9188493e-02,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

  • Attributes: (0)

  <xarray.DatasetView>
  Dimensions:        (zeuc: 50)
  Coordinates:
      xh             float64 -140.0
      yh             float64 0.0625
      yq             float64 -0.0625
    * zeuc           (zeuc) float64 -297.5 -287.5 -277.5 ... 172.5 182.5 192.5
  Data variables:
      uo             (zeuc) float32 -0.005893 0.005001 0.02487 ... nan nan nan
      vo             (zeuc) float32 -0.002198 0.0004198 0.0007794 ... nan nan nan
      ν              (zeuc) float32 5.049e-06 5.043e-06 5.044e-06 ... nan nan nan
      thetao         (zeuc) float32 10.23 10.36 10.51 10.81 ... nan nan nan nan
      Tflx_dia_diff  (zeuc) float32 1.845e-08 1.901e-08 1.982e-08 ... nan nan nan
      Kd_heat        (zeuc) float32 1.001e-06 1.001e-06 1.001e-06 ... nan nan nan
      chi            (zeuc) float32 6.837e-10 7.26e-10 7.919e-10 ... nan nan nan
      eps            (zeuc) float32 3.938e-11 4.352e-11 4.447e-11 ... nan nan nan
      S2             (zeuc) float32 2.544e-07 6.384e-07 4.584e-07 ... nan nan nan
      Rig_T          (zeuc) float32 226.6 131.1 150.5 148.7 ... nan nan nan nan

  new_baseline.kpp.lmd.005

  • Groups: (0)
  • Dimensions:
    • zeuc: 50
  • Coordinates: (4)
    • xh
      ()
      float64
      -140.0

      axis :

      X

      domain_decomposition :

      [220 222 220 221]

      long_name :

      h point nominal longitude

      units :

      degrees_east

      array(-140.)

    • yh
      ()
      float64
      0.0625

      axis :

      Y

      domain_decomposition :

      [210 258 210 221]

      long_name :

      h point nominal latitude

      units :

      degrees_north

      array(0.06249997)

    • yq
      ()
      float64
      -0.0625

      axis :

      Y

      domain_decomposition :

      [209 257 209 221]

      long_name :

      q point nominal latitude

      units :

      degrees_north

      array(-0.06249997)

    • zeuc
      (zeuc)
      float64
      -297.5 -287.5 ... 182.5 192.5

      array([-297.5, -287.5, -277.5, -267.5, -257.5, -247.5, -237.5, -227.5, -217.5,
             -207.5, -197.5, -187.5, -177.5, -167.5, -157.5, -147.5, -137.5, -127.5,
             -117.5, -107.5,  -97.5,  -87.5,  -77.5,  -67.5,  -57.5,  -47.5,  -37.5,
              -27.5,  -17.5,   -7.5,    2.5,   12.5,   22.5,   32.5,   42.5,   52.5,
               62.5,   72.5,   82.5,   92.5,  102.5,  112.5,  122.5,  132.5,  142.5,
              152.5,  162.5,  172.5,  182.5,  192.5])

  • Data variables: (10)
    • uo
      (zeuc)
      float32
      -0.005893 0.005001 ... nan nan

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Sea Water X Velocity

      standard_name :

      sea_water_x_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      array([-5.8929874e-03,  5.0010150e-03,  2.4871632e-02, -1.3677386e-05,
              4.5415116e-03,  2.2741454e-02,  6.3635116e-03,  2.4531148e-03,
              1.5222704e-02, -1.3702812e-03,  1.2736225e-02,  1.6036917e-02,
             -4.4084731e-03,  1.6050644e-02, -1.1332115e-03,  2.5234869e-02,
              1.7870275e-02,  5.3290766e-02,  7.1112156e-02,  1.2818980e-01,
              1.8416606e-01,  2.5570861e-01,  4.1997769e-01,  4.7003269e-01,
              5.9571236e-01,  7.3374313e-01,  8.8366783e-01,  1.0486763e+00,
              1.1454952e+00,  1.2573735e+00,  1.2901310e+00,  1.1977949e+00,
              8.7613827e-01,  6.3310784e-01,  4.5654660e-01,  3.0633986e-01,
              1.8709742e-01,  9.5162839e-02,  2.5411908e-02, -2.0137414e-02,
             -6.1557423e-02, -4.7269031e-02,  8.9613520e-02,  2.3487601e-01,
              1.9478835e-01,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • vo
      (zeuc)
      float32
      -0.002198 0.0004198 ... nan nan

      cell_methods :

      zl:mean yq:point xh:mean time: mean

      interp_method :

      none

      long_name :

      Sea Water Y Velocity

      standard_name :

      sea_water_y_velocity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m s-1

      array([-2.19777389e-03,  4.19814343e-04,  7.79381837e-04, -1.63761433e-03,
              5.18743531e-04,  1.12472579e-03, -1.14625844e-03,  9.12419346e-04,
              4.86612611e-04,  1.00931247e-04,  1.75473702e-04, -1.71095878e-03,
              7.74093845e-04, -5.65544877e-04,  1.16517756e-03, -3.99415003e-04,
             -1.95945366e-04, -9.65662650e-04, -1.62107041e-04, -1.39739586e-03,
              1.30683271e-04, -2.51332135e-03, -5.16847987e-03, -3.49007547e-03,
             -4.20303456e-03, -5.73705230e-03, -7.36995926e-03, -6.09641057e-03,
              1.46760372e-04, -2.56395293e-03, -8.92745517e-03, -6.60287915e-03,
             -5.58094215e-03, -1.28454212e-02, -2.20838133e-02, -2.92490181e-02,
             -3.26998085e-02, -3.21489237e-02, -2.89497189e-02, -2.41536312e-02,
             -1.76583268e-02, -1.93542968e-02, -3.83493304e-02, -1.23079315e-01,
             -1.05593860e-01,             nan,             nan,             nan,
                         nan,             nan], dtype=float32)

    • ν
      (zeuc)
      float32
      5.049e-06 5.043e-06 ... nan nan

      cell_methods :

      zl:mean yh:mean xq:point time: mean

      interp_method :

      none

      long_name :

      Total vertical viscosity at u-points

      standard_name :

      ocean_vertical_momentum_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([5.0486437e-06, 5.0429589e-06, 5.0442504e-06, 5.0475896e-06,
             5.0422968e-06, 5.0433819e-06, 5.0462104e-06, 5.0418989e-06,
             5.0425606e-06, 5.0407480e-06, 5.0382882e-06, 5.0448925e-06,
             5.0402373e-06, 5.0409162e-06, 5.0385738e-06, 5.0399153e-06,
             7.4432128e-06, 5.0412550e-06, 7.5570620e-06, 1.4555165e-05,
             1.3303173e-05, 9.6282793e-06, 1.8854163e-05, 1.3644422e-05,
             1.4976369e-05, 1.1149726e-05, 6.3058492e-06, 5.0542981e-06,
             5.0390413e-06, 6.6572366e-06, 9.9026156e-06, 4.6662979e-05,
             3.2871615e-04, 8.2944619e-04, 1.6036916e-03, 3.3080974e-03,
             6.4689615e-03, 1.0309312e-02, 1.3183054e-02, 1.3386927e-02,
             1.0917425e-02, 1.0970640e-02, 1.1653668e-02, 1.6986607e-02,
             3.7777168e-03,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • thetao
      (zeuc)
      float32
      10.23 10.36 10.51 ... nan nan nan

      cell_measures :

      volume: volcello area: areacello

      cell_methods :

      area:mean zl:mean yh:mean xh:mean time: mean

      long_name :

      Sea Water Potential Temperature

      standard_name :

      sea_water_potential_temperature

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC

      array([10.228951 , 10.357553 , 10.5078   , 10.813727 , 10.959333 ,
             11.150814 , 11.475641 , 11.667459 , 11.963285 , 12.336132 ,
             12.506242 , 12.775228 , 12.946938 , 13.233289 , 13.456113 ,
             13.663487 , 13.828789 , 13.956572 , 14.125122 , 14.294209 ,
             14.496647 , 14.8735285, 15.366182 , 15.701226 , 16.427809 ,
             16.800098 , 17.615082 , 18.836851 , 20.428156 , 20.803474 ,
             21.94135  , 23.154457 , 24.625856 , 25.270407 , 25.569725 ,
             25.756596 , 25.884304 , 25.971855 , 26.026701 , 26.067236 ,
             26.111523 , 26.287266 , 26.828798 , 27.264927 , 27.29486  ,
                    nan,        nan,        nan,        nan,        nan],
            dtype=float32)

    • Tflx_dia_diff
      (zeuc)
      float32
      1.845e-08 1.901e-08 ... nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Diffusive diapycnal temperature flux across interfaces

      standard_name :

      ocean_vertical_diffusive_heat_flux

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      degC m s-1

      array([1.84522388e-08, 1.90133331e-08, 1.98160350e-08, 2.17871534e-08,
             2.19269296e-08, 2.31987691e-08, 2.58633701e-08, 2.56593573e-08,
             2.66053508e-08, 2.70146554e-08, 2.64776752e-08, 2.54793733e-08,
             2.51310990e-08, 2.30840840e-08, 2.02013766e-08, 1.97913277e-08,
             2.01755146e-08, 2.69346945e-08, 2.62779469e-08, 5.28789563e-08,
             5.48875114e-08, 1.00109666e-07, 2.71216010e-07, 3.19583648e-07,
             3.74500303e-07, 1.46047938e-07, 1.16343891e-07, 1.03832775e-07,
             1.41212709e-07, 1.06493850e-07, 1.35873535e-07, 6.90023035e-06,
             1.85962454e-05, 2.29003454e-05, 2.42327587e-05, 2.49604745e-05,
             2.66399857e-05, 2.77722211e-05, 2.66196348e-05, 2.17321231e-05,
             1.58120238e-05, 9.36440847e-06, 1.10887204e-05, 2.19333415e-05,
             1.07785513e-06,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • Kd_heat
      (zeuc)
      float32
      1.001e-06 1.001e-06 ... nan nan

      cell_measures :

      area: areacello

      cell_methods :

      area:mean zi:point yh:mean xh:mean time: mean

      long_name :

      Total diapycnal diffusivity for heat at interfaces

      standard_name :

      ocean_vertical_heat_diffusivity

      time_avg_info :

      average_T1,average_T2,average_DT

      units :

      m2 s-1

      array([1.00067518e-06, 1.00065427e-06, 1.00066927e-06, 1.00063642e-06,
             1.00066666e-06, 1.00066268e-06, 1.00063505e-06, 1.00066575e-06,
             1.00066745e-06, 1.00066438e-06, 1.00066154e-06, 1.00063460e-06,
             1.00101636e-06, 1.00151851e-06, 1.00441002e-06, 1.06810353e-06,
             1.41212877e-06, 1.91577578e-06, 1.80881364e-06, 1.60107575e-05,
             2.37403265e-06, 4.09625409e-06, 9.22853906e-06, 2.46128784e-05,
             1.08849845e-05, 4.00248064e-06, 2.07656421e-06, 1.03766467e-06,
             1.00063471e-06, 1.10138728e-06, 1.06313692e-05, 8.53215824e-05,
             4.45734942e-04, 9.08712565e-04, 1.99472369e-03, 4.84049134e-03,
             1.07121104e-02, 1.64499823e-02, 2.01246608e-02, 1.98929477e-02,
             1.70502812e-02, 1.29411258e-02, 1.47804301e-02, 3.19275931e-02,
             6.53247582e-03,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • chi
      (zeuc)
      float32
      6.837e-10 7.26e-10 ... nan nan

      long_name :

      $χ$

      units :

      C^2/s

      array([6.8373696e-10, 7.2600542e-10, 7.9185863e-10, 9.5713926e-10,
             9.6860342e-10, 1.0881023e-09, 1.3508273e-09, 1.3291162e-09,
             1.4311416e-09, 1.4834852e-09, 1.4281516e-09, 1.3238612e-09,
             1.3013337e-09, 1.1062754e-09, 8.5938778e-10, 8.0222251e-10,
             6.7409117e-10, 8.7976160e-10, 9.4508401e-10, 2.2733280e-09,
             3.1751306e-09, 6.8161152e-09, 2.0803506e-08, 2.8093355e-08,
             3.7279484e-08, 1.6553953e-08, 1.9269990e-08, 2.3974705e-08,
             4.3170928e-08, 2.6435435e-08, 3.5509224e-08, 1.4844380e-06,
             2.2649865e-06, 1.8430437e-06, 1.2526549e-06, 7.6951369e-07,
             4.7593264e-07, 3.4695140e-07, 3.5711125e-07, 4.3343695e-07,
             4.5873477e-07, 4.6846111e-07, 4.6900448e-07, 2.3183331e-07,
             4.6221166e-07,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • eps
      (zeuc)
      float32
      3.938e-11 4.352e-11 ... nan nan

      long_name :

      $ε$

      units :

      W/kg

      array([3.9382265e-11, 4.3520621e-11, 4.4468890e-11, 4.9679094e-11,
             5.0937501e-11, 5.5393547e-11, 6.1908964e-11, 6.2446576e-11,
             6.6434858e-11, 6.7113939e-11, 6.4494368e-11, 6.1906279e-11,
             6.0327979e-11, 5.5024558e-11, 5.1699981e-11, 7.5435810e-11,
             7.5297879e-11, 1.4965926e-10, 2.8711039e-10, 4.2676571e-10,
             5.8342109e-10, 1.5079648e-09, 2.8221063e-09, 2.9051124e-09,
             4.2336281e-09, 2.1570059e-09, 1.5742398e-09, 1.4037076e-09,
             1.5026101e-09, 8.5145674e-10, 2.4366905e-08, 3.5581684e-07,
             3.2794830e-07, 4.5730107e-07, 5.0692626e-07, 4.9011328e-07,
             4.2123892e-07, 3.4453245e-07, 3.3985438e-07, 3.6136080e-07,
             3.8029071e-07, 4.1178455e-07, 4.1902607e-07, 2.2534654e-07,
             4.4791264e-07,           nan,           nan,           nan,
                       nan,           nan], dtype=float32)

    • S2
      (zeuc)
      float32
      2.544e-07 6.384e-07 ... nan nan

      long_name :

      $S^2$

      units :

      s$^{-2}$

      array([2.54378733e-07, 6.38353811e-07, 4.58427763e-07, 3.80050096e-07,
             5.64058951e-07, 6.36048640e-07, 5.56854729e-07, 7.26399946e-07,
             9.05369006e-07, 9.45442423e-07, 8.51951029e-07, 6.91803280e-07,
             7.55714382e-07, 7.04647334e-07, 1.26243197e-06, 2.71247109e-06,
             4.65858102e-06, 1.16520087e-05, 1.99510814e-05, 3.31415249e-05,
             5.00230417e-05, 8.05205273e-05, 1.27244726e-04, 1.69002291e-04,
             2.03346484e-04, 2.10992672e-04, 1.87548270e-04, 1.89796614e-04,
             2.03424614e-04, 5.08211524e-05, 1.83125841e-04, 1.33370166e-03,
             6.43725973e-04, 4.54566209e-04, 3.34972632e-04, 2.33477040e-04,
             1.48811640e-04, 1.01950354e-04, 9.15324563e-05, 9.83889113e-05,
             1.07818625e-04, 1.17441712e-04, 1.18318312e-04, 4.21129625e-05,
             1.39109572e-04,            nan,            nan,            nan,
                        nan,            nan], dtype=float32)

    • Rig_T
      (zeuc)
      float32
      226.6 131.1 150.5 ... nan nan nan

      long_name :

      $Ri^g_T$

      array([ 2.26648071e+02,  1.31057510e+02,  1.50471497e+02,  1.48722946e+02,
              1.45639999e+02,  1.06133774e+02,  1.36979568e+02,  1.12499794e+02,
              8.02135468e+01,  8.84140244e+01,  8.54823303e+01,  1.19611740e+02,
              9.99403992e+01,  1.19359146e+02,  7.77576904e+01,  4.18875122e+01,
              1.76356297e+01,  4.95762348e+00,  2.53250837e+00,  1.47906017e+00,
              1.34763014e+00,  9.56021309e-01,  7.78292775e-01,  6.81925893e-01,
              6.09881163e-01,  6.72925949e-01,  9.91986275e-01,  1.26318133e+00,
              1.82970357e+00,  8.63165855e+00,  6.78957748e+00,  3.14613789e-01,
              3.16555321e-01,  2.50890136e-01,  2.09905684e-01,  2.02570021e-01,
              2.29070097e-01,  2.94496417e-01,  3.63696158e-01,  3.25371534e-01,
              2.26018295e-01,  1.37651816e-01,  2.03731701e-01,  3.91990632e-01,
             -2.14977749e-03,             nan,             nan,             nan,
                         nan,             nan], dtype=float32)

  • Attributes: (0)

  <xarray.DatasetView>
  Dimensions:    (zeuc: 80)
  Coordinates:
      longitude  int64 -140
      latitude   int64 0
    * zeuc       (zeuc) float64 -200.0 -195.0 -190.0 -185.0 ... 185.0 190.0 195.0
  Data variables:
      u          (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
      v          (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
      chi        (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
      eps        (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
      theta      (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
      Sh2        (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
      Tz         (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
      N2T        (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
      Rig_T      (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
      Shred2     (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
      KT         (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
      ν          (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan
  Attributes:
      starttime:  ['' '' '' ... 'Time:22:51:04 328   ' 'Time:22:57:35 328   '\n...
      endtime:    ['' '' '' ... 'Time:22:54:39 328   ' 'Time:23:01:10 328   '\n...
      readme:     ['EPSILON_clean cleaned using tw91_eps_chi_sum1.mat          ...
      name:       TIWE

  TIWE

  • Groups: (0)
  • Dimensions:
    • zeuc: 80
  • Coordinates: (3)
    • longitude
      ()
      int64
      -140

      units :

      degrees_east

      standard_name :

      longitude

      array(-140)

    • latitude
      ()
      int64
      0

      units :

      degrees_north

      standard_name :

      latitude

      array(0)

    • zeuc
      (zeuc)
      float64
      -200.0 -195.0 ... 190.0 195.0

      array([-200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
             -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
             -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
              -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
                0.,    5.,   10.,   15.,   20.,   25.,   30.,   35.,   40.,   45.,
               50.,   55.,   60.,   65.,   70.,   75.,   80.,   85.,   90.,   95.,
              100.,  105.,  110.,  115.,  120.,  125.,  130.,  135.,  140.,  145.,
              150.,  155.,  160.,  165.,  170.,  175.,  180.,  185.,  190.,  195.])

  • Data variables: (12)
    • u
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      sea_water_x_velocity

      array([            nan,             nan,             nan,             nan,
                         nan,             nan,             nan, -1.05116857e-01,
             -8.78450123e-02, -6.91070307e-02, -5.93865995e-02, -3.65194950e-02,
             -3.06684922e-02, -3.16731008e-02, -1.45563634e-02, -1.04912230e-02,
              1.20081768e-02,  3.44010920e-02,  6.06641421e-02,  8.70872427e-02,
              1.14793442e-01,  1.43454882e-01,  1.73844347e-01,  2.06472059e-01,
              2.41247369e-01,  2.80579111e-01,  3.23215957e-01,  3.69795305e-01,
              4.17526619e-01,  4.64899298e-01,  5.10044100e-01,  5.53948504e-01,
              5.97009535e-01,  6.39443865e-01,  6.81379689e-01,  7.22712552e-01,
              7.63980042e-01,  8.04971029e-01,  8.45197885e-01,  8.73555811e-01,
              8.88815998e-01,  8.71057848e-01,  8.38077529e-01,  7.90123658e-01,
              7.42882018e-01,  6.95473878e-01,  6.46854003e-01,  5.93667994e-01,
              5.36247881e-01,  4.71006770e-01,  4.01361037e-01,  3.26968218e-01,
              2.52710497e-01,  1.80637561e-01,  1.19099493e-01,  8.03703500e-02,
              2.75534902e-02,  7.20406459e-04, -3.41486551e-02, -4.16155778e-02,
             -2.64509151e-02, -4.66886898e-02,  5.56134438e-03,  1.38017142e-03,
             -1.42177546e-02, -7.06127976e-02, -8.76528601e-02,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan])

    • v
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      sea_water_y_velocity

      array([        nan,         nan,         nan,         nan,         nan,
                     nan,         nan, -0.0558892 , -0.04796128, -0.05296166,
             -0.05187718, -0.05491471, -0.05786042, -0.05031479, -0.05070498,
             -0.05010691, -0.05009191, -0.04864032, -0.04718164, -0.04498721,
             -0.04109011, -0.03560548, -0.02710676, -0.01679335, -0.0053954 ,
              0.00591623,  0.01682267,  0.02704529,  0.03553181,  0.04248355,
              0.04656708,  0.04639887,  0.04212086,  0.02909312,  0.00998746,
             -0.01614186, -0.04189481, -0.06243535, -0.067715  , -0.06181233,
             -0.04935251, -0.0389986 , -0.03414252, -0.03764416, -0.05212121,
             -0.07260562, -0.10013674, -0.12669765, -0.15128597, -0.1691306 ,
             -0.18148522, -0.18683141, -0.18550657, -0.18087429, -0.17383764,
             -0.17143653, -0.16175374, -0.16328311, -0.15892244, -0.14943801,
             -0.11895927, -0.1219241 , -0.14898413, -0.20467592, -0.20930846,
             -0.30027357, -0.30225361,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan])

    • chi
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      long_name :

      $χ$

      units :

      °C²/s

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan, 1.91401590e-09,
             8.73531007e-08, 1.53549044e-08, 1.13987005e-08, 7.03480507e-08,
             3.93812460e-08, 5.69824765e-08, 8.18564238e-08, 3.64788931e-08,
             2.77012136e-08, 5.39047386e-08, 1.13209980e-07, 1.75043127e-07,
             3.58997616e-07, 2.33415460e-07, 3.04582011e-07, 3.13033807e-07,
             3.38606638e-07, 3.92352344e-07, 3.32935266e-07, 1.62167490e-07,
             1.21872951e-07, 7.37120413e-08, 5.97562621e-08, 7.17358497e-08,
             1.63791402e-07, 1.88997112e-07, 1.60624235e-07, 1.43209813e-07,
             1.59526349e-07, 2.48843309e-07, 3.76894085e-07, 3.66777005e-07,
             2.70499440e-07, 2.55378151e-07, 1.91707271e-07, 1.37017866e-07,
             1.27049993e-07, 2.49129621e-07, 1.00105959e-07, 1.65623701e-07,
             1.20560773e-07, 1.44200478e-07, 1.75966048e-07, 5.08732810e-07,
             1.21455284e-06, 1.64655056e-06, 3.09695489e-08, 6.56333657e-08,
             2.32187670e-09,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan])

    • eps
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      long_name :

      $ε$

      units :

      W/kg

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
             1.04672174e-09, 2.46027970e-09, 1.12435161e-09, 1.28390414e-09,
             1.00616878e-09, 1.03519860e-09, 2.96260747e-09, 2.08299195e-09,
             5.10680596e-09, 7.47771585e-09, 1.57125503e-08, 7.88768013e-09,
             8.44971424e-09, 4.95405167e-09, 6.47206807e-09, 7.24672927e-09,
             7.54171659e-09, 6.65710039e-09, 5.62058178e-09, 4.73399454e-09,
             7.06065555e-09, 3.53968618e-09, 4.21119364e-09, 4.99713646e-09,
             9.33822508e-09, 5.78856419e-09, 7.65369289e-09, 9.16852766e-09,
             1.66989484e-08, 1.66589623e-08, 1.43223281e-08, 1.27336669e-08,
             1.65764687e-08, 1.45352688e-08, 1.73806699e-08, 1.44375924e-08,
             3.41709034e-08, 8.32098814e-08, 1.44047673e-07, 2.14100181e-07,
             2.97231235e-07, 3.63849720e-07, 4.39013202e-07, 4.86744389e-07,
             5.54237391e-07, 5.81733022e-07, 6.24812775e-07, 6.33522725e-07,
             6.83208750e-07, 1.03267754e-06, 7.95513562e-07, 9.53926772e-07,
             1.49332115e-06, 1.57735953e-06, 3.85363994e-06, 3.48126797e-06,
             7.35445242e-06, 5.35391261e-06, 5.19481001e-06, 1.93969621e-05,
             5.62386531e-08,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan])

    • theta
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      sea_water_potential_temperature

      units :

      celsius

      array([        nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan, 12.27027015, 12.31477067,
             12.60446926, 12.61411099, 12.5681978 , 12.5983967 , 12.64335497,
             12.69678939, 12.85765037, 13.00071564, 13.20561109, 13.37259677,
             13.47598497, 13.52191615, 13.77936605, 13.90548921, 14.07952953,
             14.33257073, 14.63686104, 14.99768353, 15.38784395, 15.87271286,
             16.48411809, 17.19243887, 18.04835956, 18.95035525, 19.90508981,
             20.93727464, 21.89309668, 22.80955607, 23.67475671, 24.41619341,
             25.04854397, 25.56969387, 25.96256023, 26.22942591, 26.42898395,
             26.56141693, 26.64492918, 26.70014623, 26.73929252, 26.77195295,
             26.81311731, 26.86566579, 26.91848789, 26.96277522, 27.00184556,
             27.03331133, 27.05972579, 27.08007154, 27.0952071 , 27.1081003 ,
             27.11752904, 27.122601  , 27.1254335 , 27.12694655, 27.11912955,
             27.13898699, 27.14391606, 27.1580728 , 27.17226852,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan])

    • Sh2
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
             2.41936817e-05, 2.63153069e-05, 3.56675431e-05, 2.53119561e-05,
             2.23424096e-05, 1.98043466e-05, 1.84729444e-05, 2.19671534e-05,
             2.55924596e-05, 3.08315806e-05, 3.57826475e-05, 3.92997466e-05,
             4.34068094e-05, 4.95598280e-05, 5.85714661e-05, 6.75501495e-05,
             7.88186807e-05, 9.27058518e-05, 1.04684731e-04, 1.12020167e-04,
             1.12273605e-04, 1.07391366e-04, 1.04506594e-04, 1.07225977e-04,
             1.12363100e-04, 1.23013305e-04, 1.33253630e-04, 1.40435138e-04,
             1.43914091e-04, 1.29277376e-04, 9.69778004e-05, 5.85841049e-05,
             3.12493265e-05, 5.75280338e-05, 1.07158583e-04, 1.51674537e-04,
             1.67300940e-04, 1.77406931e-04, 1.90591406e-04, 2.04971901e-04,
             2.23599647e-04, 2.38536376e-04, 2.43809555e-04, 2.41444550e-04,
             2.30190300e-04, 2.11015658e-04, 1.83191649e-04, 1.48703654e-04,
             1.19003331e-04, 8.52937072e-05, 7.24674164e-05, 6.83340417e-05,
             4.64024226e-05, 6.59667504e-05, 9.51011300e-05, 3.11837692e-05,
             6.31130409e-05, 2.33018435e-05,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan])

    • Tz
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      array([       nan,        nan,        nan,        nan,        nan,
                    nan,        nan,        nan,        nan, 0.01319911,
             0.01055727, 0.00408227, 0.00488797, 0.00833917, 0.00741158,
             0.00619215, 0.00669396, 0.01442379, 0.02392398, 0.03373917,
             0.0364512 , 0.0419686 , 0.04242911, 0.04994164, 0.0578379 ,
             0.06513217, 0.07386418, 0.07939579, 0.08917988, 0.11031311,
             0.13354579, 0.15769182, 0.17695173, 0.18580005, 0.19878145,
             0.19871063, 0.18724572, 0.178166  , 0.16066373, 0.13737873,
             0.11535005, 0.09140163, 0.06596186, 0.04663675, 0.03319472,
             0.02159774, 0.01388406, 0.00943907, 0.00718388, 0.00742707,
             0.00940052, 0.01054695, 0.00990347, 0.00847902, 0.00724818,
             0.00586937, 0.00505891, 0.00477037, 0.00396721, 0.00339247,
             0.00320092, 0.00324608, 0.00281531, 0.00241565, 0.002715  ,
             0.00087841, 0.00044906, 0.00022746,        nan,        nan,
                    nan,        nan,        nan,        nan,        nan,
                    nan,        nan,        nan,        nan,        nan])

    • N2T
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      units :

      celsius

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan, 2.42101685e-05, 1.96627179e-05, 7.61049420e-06,
             9.09576015e-06, 1.55491325e-05, 1.38574145e-05, 1.16139629e-05,
             1.26612997e-05, 2.74865039e-05, 4.60659926e-05, 6.55148621e-05,
             7.11595780e-05, 8.21432717e-05, 8.40681817e-05, 9.95655518e-05,
             1.16259825e-04, 1.32449602e-04, 1.52261760e-04, 1.66248694e-04,
             1.89831521e-04, 2.39509079e-04, 2.97038622e-04, 3.60333355e-04,
             4.17217932e-04, 4.52149709e-04, 4.99510667e-04, 5.16165310e-04,
             5.00854737e-04, 4.89559732e-04, 4.52377409e-04, 3.94718017e-04,
             3.37037767e-04, 2.70711323e-04, 1.97346088e-04, 1.40481314e-04,
             1.00497690e-04, 6.56088486e-05, 4.22684175e-05, 2.87789886e-05,
             2.19272064e-05, 2.26908933e-05, 2.87529707e-05, 3.23051213e-05,
             3.03770943e-05, 2.60393452e-05, 2.22835101e-05, 1.80608336e-05,
             1.55791284e-05, 1.46999338e-05, 1.22312804e-05, 1.04641459e-05,
             9.87706905e-06, 1.00192077e-05, 8.69157880e-06, 7.45918718e-06,
             8.38334162e-06, 2.71403662e-06, 1.38783523e-06, 7.03326675e-07,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan])

    • Rig_T
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      units :

      celsius

      array([        nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,  1.99202459,
              1.69606609,  0.56792074,  0.42689249,  0.51455311,  0.40304173,
              0.29015853,  0.23716566,  0.51976126,  0.86862424,  1.55562163,
              1.20180377,  1.15973112,  1.17351158,  1.23276115,  1.23035239,
              1.15519236,  1.07573885,  1.03428242,  1.28023235,  1.91065333,
              2.7633041 ,  3.41448597,  3.76235454,  4.11676705,  4.10232985,
              4.20455427,  3.92901596,  4.72556873,  5.88266212,  8.82068599,
             14.15497927,  4.33564004,  1.21310963,  0.62287698,  0.44596426,
              0.27578452,  0.15370404,  0.08466717,  0.06990047,  0.07796601,
              0.09266341,  0.10296131,  0.10505684,  0.10214939,  0.09734437,
              0.10989984,  0.12384845,  0.15676421,  0.16643647,  0.16322899,
              0.26793743,  0.2305186 ,  0.13040248,  0.26167545,  0.27165878,
              0.30828336,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan])

    • Shred2
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      array([            nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan, -8.46871250e-05, -6.70577397e-05, -1.81609661e-05,
             -2.47504947e-05, -5.24407007e-05, -4.71447447e-05, -3.60356112e-05,
             -3.60513691e-05, -8.50070432e-05, -1.49780057e-04, -2.15055522e-04,
             -2.22929408e-04, -2.58418750e-04, -2.61600901e-04, -3.14968692e-04,
             -3.78199509e-04, -4.33549024e-04, -5.02047643e-04, -5.51514803e-04,
             -6.47338150e-04, -8.52258354e-04, -1.08612286e-03, -1.33591904e-03,
             -1.55803782e-03, -1.68500092e-03, -1.86344321e-03, -1.92265385e-03,
             -1.85813608e-03, -1.82851757e-03, -1.71277002e-03, -1.52039380e-03,
             -1.31661436e-03, -1.02461127e-03, -6.81412302e-04, -4.09453971e-04,
             -2.34192306e-04, -8.50317932e-05,  2.09196167e-05,  8.89778649e-05,
              1.35051531e-04,  1.46642996e-04,  1.27572298e-04,  1.11304380e-04,
              1.09318793e-04,  1.07693281e-04,  9.53061106e-05,  7.77479600e-05,
              5.87095732e-05,  3.01430978e-05,  2.57882192e-05,  1.94371501e-05,
              5.23228687e-06,  1.90434434e-05,  3.86272281e-05, -5.51034484e-06,
             -5.46780544e-06, -5.43243899e-06,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan])

    • KT
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      long_name :

      KT

      units :

      °C²/s

      standard_name :

      ocean_vertical_heat_diffusivity

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
             3.11034257e-06, 7.27904762e-07, 7.10238685e-07, 3.85490340e-06,
             7.62858065e-06, 4.23798353e-05, 4.26767225e-04, 1.92089081e-05,
             3.21080997e-05, 1.68690567e-05, 2.60959572e-04, 5.25633464e-04,
             1.08588017e-04, 3.56067721e-03, 1.54237668e-04, 1.38328616e-03,
             2.17445864e-03, 3.35261789e-05, 9.29949055e-05, 1.15035765e-04,
             1.27553983e-04, 3.47318661e-04, 4.74906977e-04, 5.43061763e-04,
             1.28363424e-03, 2.14045291e-03, 3.90373232e-03, 1.82978622e-03,
             2.49007649e-03, 5.53119349e-03, 4.12953073e-03, 3.21330613e-03,
             4.31628907e-03, 1.98163881e-03, 3.30108160e-03, 3.77763230e-03,
             1.05707982e-02, 8.16199487e-03, 7.87422784e-03, 1.03365032e-02,
             2.47680837e-02, 1.68225290e-02, 1.66146718e-02, 3.34472636e-02,
             4.69596923e-02, 6.39420715e-02, 4.61705915e-02, 5.40689455e-02,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan])

    • ν
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      long_name :

      ν

      units :

      W/kg

      standard_name :

      ocean_vertical_momentum_diffusivity

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan, 2.02433025e-04, 6.52778817e-05, 6.56160982e-05,
             8.95604155e-05, 7.05365382e-05, 3.98064010e-04, 1.52595744e-04,
             1.91511453e-04, 6.08683341e-04, 7.73383850e-04, 3.66858201e-04,
             3.20232402e-04, 1.13794554e-04, 1.18652299e-04, 1.40950282e-04,
             1.54905537e-04, 9.73632499e-05, 6.87962805e-05, 5.31487061e-05,
             1.09741835e-04, 4.98080835e-05, 6.27093545e-05, 7.50910423e-05,
             1.70996185e-04, 9.45308237e-05, 9.90430163e-05, 1.20742021e-04,
             2.10593434e-04, 2.11730363e-04, 2.07588177e-04, 3.26472478e-04,
             6.62253420e-04, 3.79178084e-04, 2.28515027e-04, 1.16251384e-04,
             2.70686576e-04, 3.60658923e-04, 7.08755437e-04, 1.27751012e-03,
             1.43025791e-03, 1.76987787e-03, 2.19161082e-03, 2.35499497e-03,
             2.87559556e-03, 3.38494818e-03, 3.65112011e-03, 4.99381967e-03,
             6.09707376e-03, 8.01378558e-03, 1.10191331e-02, 9.18495381e-03,
             1.21835999e-02, 1.51177039e-02, 9.63162607e-03, 3.72289668e-02,
             5.69256543e-03, 2.53126173e-03,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan])

  • Attributes: (4)

    starttime :

    ['' '' '' ... 'Time:22:51:04 328 ' 'Time:22:57:35 328 ' 'Time:23:04:04 328 ']

    endtime :

    ['' '' '' ... 'Time:22:54:39 328 ' 'Time:23:01:10 328 ' 'Time:23:07:49 328 ']

    readme :

    ['EPSILON_clean cleaned using tw91_eps_chi_sum1.mat ' '(all that is marked NaN or missed in tw91_eps_chi_sum1.mat ' 'is marked NaN in that field too) plus bad_drops.40, ' 'which contained contaminated casts, is used to mark bad EPSILON']

    name :

    TIWE

  <xarray.DatasetView>
  Dimensions:  (zeuc: 80)
  Coordinates:
    * zeuc     (zeuc) float64 -200.0 -195.0 -190.0 -185.0 ... 185.0 190.0 195.0
  Data variables:
      u        (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
      v        (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
      chi      (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
      eps      (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
      theta    (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
      Sh2      (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
      Tz       (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
      N2T      (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
      Rig_T    (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
      Shred2   (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
      KT       (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
      ν        (zeuc) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan
  Attributes:
      starttime:  ['Time:20:34:29 298   ' 'Time:20:42:18 298   ' 'Time:20:52:14...
      endtime:    ['Time:20:38:29 298   ' 'Time:20:46:29 298   ' 'Time:20:56:29...
      name:       EQUIX

  EQUIX

  • Groups: (0)
  • Dimensions:
    • zeuc: 80
  • Coordinates: (1)
    • zeuc
      (zeuc)
      float64
      -200.0 -195.0 ... 190.0 195.0

      array([-200., -195., -190., -185., -180., -175., -170., -165., -160., -155.,
             -150., -145., -140., -135., -130., -125., -120., -115., -110., -105.,
             -100.,  -95.,  -90.,  -85.,  -80.,  -75.,  -70.,  -65.,  -60.,  -55.,
              -50.,  -45.,  -40.,  -35.,  -30.,  -25.,  -20.,  -15.,  -10.,   -5.,
                0.,    5.,   10.,   15.,   20.,   25.,   30.,   35.,   40.,   45.,
               50.,   55.,   60.,   65.,   70.,   75.,   80.,   85.,   90.,   95.,
              100.,  105.,  110.,  115.,  120.,  125.,  130.,  135.,  140.,  145.,
              150.,  155.,  160.,  165.,  170.,  175.,  180.,  185.,  190.,  195.])

  • Data variables: (12)
    • u
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      sea_water_x_velocity

      array([        nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,  0.22115788,  0.22654945,  0.29191623,  0.34394949,
              0.3729316 ,  0.38517439,  0.40121283,  0.42977609,  0.4662755 ,
              0.50906621,  0.55494024,  0.60390715,  0.65761372,  0.71461533,
              0.77128995,  0.83032185,  0.89511044,  0.96070988,  1.02436224,
              1.0902707 ,  1.1546409 ,  1.21010393,  1.26473946,  1.3234391 ,
              1.36673891,  1.30642576,  1.18996499,  1.05949794,  0.9322365 ,
              0.80925976,  0.69112074,  0.58224709,  0.48456937,  0.39901329,
              0.32261591,  0.2510615 ,  0.17860259,  0.10402496,  0.02954474,
             -0.03894944, -0.09192208, -0.13323597, -0.17259708, -0.21783335,
             -0.2211524 , -0.17832167, -0.05484485,  0.20941225,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan])

    • v
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      sea_water_y_velocity

      array([        nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan, -0.10866021, -0.08189667, -0.0415053 , -0.01654078,
             -0.01723334, -0.02194068, -0.01866929, -0.01492488, -0.00601155,
              0.00588569,  0.01844987,  0.03073579,  0.04198478,  0.05074239,
              0.05639361,  0.05873327,  0.05652131,  0.05013584,  0.04227849,
              0.03166786,  0.0239972 ,  0.02739307,  0.03479125,  0.03245866,
              0.01181622, -0.02180246, -0.05192954, -0.07314375, -0.08928198,
             -0.10649081, -0.12856883, -0.15245701, -0.17388981, -0.19018318,
             -0.20160852, -0.20642006, -0.20513149, -0.19960923, -0.19233737,
             -0.18394684, -0.1725463 , -0.16773698, -0.17698639, -0.22702554,
             -0.19803006, -0.23943411, -0.08621481,  0.66289347,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan])

    • chi
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      long_name :

      $χ$

      units :

      °C²/s

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
             1.14494094e-10, 1.31958541e-10, 1.91693766e-08, 1.46419404e-08,
             1.21253803e-08, 1.32856317e-08, 1.23397882e-08, 2.91354841e-08,
             3.07858540e-08, 3.36049027e-08, 2.27920876e-08, 2.44964076e-08,
             1.78190065e-08, 1.75669311e-08, 2.85808906e-08, 1.49093214e-07,
             3.63759931e-07, 3.48106623e-07, 1.86058880e-07, 9.86207350e-08,
             1.79307372e-07, 2.70481800e-07, 1.95429169e-07, 1.76120062e-07,
             2.26235626e-07, 2.08672841e-06, 4.26421905e-06, 4.72083990e-06,
             4.78804391e-06, 5.69365153e-06, 5.86931860e-06, 4.85199160e-06,
             5.51234583e-06, 4.97715339e-06, 1.33977243e-05, 2.20134844e-06,
             2.81166126e-06, 1.50208453e-06, 1.38140749e-06, 7.40429394e-06,
             1.20634636e-06, 3.05004447e-06, 1.62756379e-04, 2.59531267e-06,
             2.95864739e-06, 1.51474209e-06, 2.99036803e-06, 3.76726187e-06,
             4.12241223e-07, 4.01809429e-07,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan])

    • eps
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      long_name :

      $ε$

      units :

      W/kg

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
             5.32419426e-09, 2.45807692e-09, 4.10523158e-09, 4.09770973e-09,
             4.35521884e-09, 3.99843721e-09, 6.32214805e-09, 5.35798845e-09,
             6.72184206e-09, 6.93901553e-09, 6.31028114e-09, 5.87525040e-09,
             5.71214151e-09, 5.18111550e-09, 5.13345064e-09, 7.72822196e-09,
             1.92496201e-08, 1.86357852e-08, 1.39084977e-08, 9.70494225e-09,
             1.33954526e-08, 1.69618118e-08, 2.04721725e-08, 1.83251281e-08,
             2.66416423e-08, 2.92761399e-07, 7.80004637e-07, 1.18254903e-06,
             1.35894506e-06, 1.53038001e-06, 1.54821424e-06, 1.49303914e-06,
             1.41502900e-06, 1.47521170e-06, 1.45609523e-06, 1.52113361e-06,
             1.57851807e-06, 1.62467500e-06, 1.61982032e-06, 1.65306750e-06,
             1.81416930e-06, 2.38185475e-06, 3.09447452e-06, 4.57120561e-06,
             5.21569277e-06, 7.15001194e-06, 9.22694251e-06, 8.08168171e-06,
             6.98544200e-06,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan])

    • theta
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      standard_name :

      sea_water_potential_temperature

      units :

      celsius

      array([        nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan, 12.87624238, 13.01483644, 13.15381329, 13.24011816,
             13.29555921, 13.30639973, 13.34170064, 13.43829315, 13.58342779,
             13.76042136, 13.93697868, 14.14579384, 14.38379946, 14.63648322,
             14.92743999, 15.28545856, 15.74013707, 16.2102157 , 16.65955107,
             17.16281705, 17.75368041, 18.38168465, 18.85633917, 19.35605905,
             20.01964621, 20.75432354, 21.39860009, 21.88022006, 22.29196158,
             22.66613606, 23.02317819, 23.3730267 , 23.7091678 , 24.0107391 ,
             24.25476328, 24.44696177, 24.59248381, 24.71203164, 24.81512293,
             24.90538273, 24.98098258, 25.03964729, 25.07854227, 25.10922601,
             25.12141223, 25.1390445 , 25.11738002, 25.04148045, 24.91115893,
             24.87990414,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan,
                     nan,         nan,         nan,         nan,         nan])

    • Sh2
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan, 1.46805278e-05, 2.06247109e-05, 2.79099890e-05,
             4.23866561e-05, 5.11994357e-05, 6.33161535e-05, 7.02602956e-05,
             8.38146306e-05, 9.79962718e-05, 1.10410829e-04, 1.25932413e-04,
             1.45260588e-04, 1.56914383e-04, 1.63750445e-04, 1.92023306e-04,
             2.22768575e-04, 2.20763479e-04, 2.20006645e-04, 2.36385131e-04,
             2.09275090e-04, 1.73425476e-04, 1.80434080e-04, 1.81536156e-04,
             1.79866699e-04, 6.27124566e-04, 9.32668894e-04, 9.12921742e-04,
             8.10805421e-04, 7.32960667e-04, 6.63357940e-04, 5.79124548e-04,
             4.88951792e-04, 3.97653283e-04, 3.19528934e-04, 2.80381527e-04,
             2.73227861e-04, 2.78100963e-04, 2.71834086e-04, 2.45855867e-04,
             2.10678972e-04, 1.72479992e-04, 1.53969878e-04, 1.11599259e-04,
             8.92671008e-05, 4.89363817e-05, 1.42668450e-05,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan])

    • Tz
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      array([            nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,  3.44367380e-03,  6.22888616e-03,  1.04712061e-02,
              1.57161617e-02,  1.82320296e-02,  2.33686425e-02,  3.01791295e-02,
              3.75057690e-02,  4.07030220e-02,  4.31830180e-02,  4.73058646e-02,
              5.01065707e-02,  5.45995780e-02,  6.49529271e-02,  8.12991949e-02,
              9.24973936e-02,  9.18497953e-02,  9.51663275e-02,  1.09394235e-01,
              1.21876151e-01,  1.10245938e-01,  9.74265472e-02,  1.16330704e-01,
              1.39825363e-01,  1.37861577e-01,  1.12509470e-01,  8.92971678e-02,
              7.85915995e-02,  7.31132631e-02,  7.06607877e-02,  6.86087732e-02,
              6.38010404e-02,  5.45427007e-02,  4.36279223e-02,  3.37846019e-02,
              2.64612821e-02,  2.21992763e-02,  1.93526017e-02,  1.66171053e-02,
              1.36099913e-02,  1.11664335e-02,  9.35016297e-03,  8.20059551e-03,
              6.34605231e-03,  5.02814275e-03,  3.56957173e-03,  1.98858752e-03,
             -4.23771129e-05,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan])

    • N2T
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      units :

      celsius

      array([            nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,  6.56721408e-06,  1.19643843e-05,  2.02057808e-05,
              3.04211779e-05,  3.53250677e-05,  4.53737309e-05,  5.88906580e-05,
              7.37172501e-05,  8.06924547e-05,  8.63397784e-05,  9.55211629e-05,
              1.02298239e-04,  1.12757311e-04,  1.35879203e-04,  1.72720834e-04,
              2.00283801e-04,  2.02711343e-04,  2.13789148e-04,  2.50548854e-04,
              2.85333614e-04,  2.64005710e-04,  2.37222934e-04,  2.88109271e-04,
              3.53930875e-04,  3.57196413e-04,  2.97354435e-04,  2.39448443e-04,
              2.13319176e-04,  2.00622381e-04,  1.95890338e-04,  1.92094066e-04,
              1.80316353e-04,  1.55437323e-04,  1.25164913e-04,  9.74349545e-05,
              7.66192410e-05,  6.44902443e-05,  5.63807491e-05,  4.85326304e-05,
              3.98341494e-05,  3.27371421e-05,  2.74444120e-05,  2.40933475e-05,
              1.86538501e-05,  1.47891933e-05,  1.04952643e-05,  5.83671031e-06,
             -1.23994795e-07,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan])

    • Rig_T
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      units :

      celsius

      array([       nan,        nan,        nan,        nan,        nan,
                    nan,        nan,        nan,        nan,        nan,
                    nan,        nan,        nan,        nan,        nan,
                    nan,        nan, 0.69034727, 0.54732533, 0.67701297,
             0.66380184, 0.59054073, 0.59983133, 0.71771542, 0.81118442,
             0.80488672, 0.78706258, 0.77798954, 0.70695326, 0.70017751,
             0.87434193, 0.95134817, 0.90612647, 0.91506717, 1.02694396,
             1.13293163, 1.51519289, 1.6748384 , 1.46195153, 1.75690015,
             2.69457106, 0.60679189, 0.34545704, 0.24627558, 0.23295552,
             0.22900769, 0.23821688, 0.26834039, 0.29961545, 0.31962308,
             0.31103159, 0.26839199, 0.23462162, 0.21331184, 0.20353174,
             0.2067661 , 0.21799492, 0.23800667, 0.25086958, 0.3151814 ,
             0.39298945, 0.60493939, 0.41753704,        nan,        nan,
                    nan,        nan,        nan,        nan,        nan,
                    nan,        nan,        nan,        nan,        nan,
                    nan,        nan,        nan,        nan,        nan])

    • Shred2
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      array([            nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan, -9.41249006e-06, -2.26975495e-05, -4.88815768e-05,
             -7.55817321e-05, -8.66440345e-05, -1.14995917e-04, -1.61245757e-04,
             -2.08176699e-04, -2.23806639e-04, -2.35229015e-04, -2.57068084e-04,
             -2.64991984e-04, -2.94913958e-04, -3.80309518e-04, -4.98993560e-04,
             -5.78022658e-04, -5.89577378e-04, -6.34761977e-04, -7.65620370e-04,
             -9.32094472e-04, -8.82516540e-04, -7.68429789e-04, -9.70903378e-04,
             -1.23664331e-03, -8.03395454e-04, -2.57748577e-04, -4.51092058e-05,
             -4.24450350e-05, -6.92698022e-05, -1.20411670e-04, -1.90716746e-04,
             -2.33448100e-04, -2.23928057e-04, -1.80979909e-04, -1.09652256e-04,
             -3.35175226e-05,  1.95853141e-05,  4.51908428e-05,  4.75623258e-05,
              4.02442053e-05,  2.86345504e-05,  2.32317480e-05, -2.40244511e-06,
             -1.34170855e-05, -2.28041784e-05, -1.24076878e-05,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan,
                         nan,             nan,             nan,             nan])

    • KT
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      long_name :

      KT

      units :

      °C²/s

      standard_name :

      ocean_vertical_heat_diffusivity

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan, 1.75063220e-06, 1.54973389e-06, 3.94160465e-05,
             1.79327457e-05, 1.91694538e-05, 1.61177356e-05, 1.34758569e-05,
             8.34794199e-06, 1.77731359e-05, 1.36453579e-05, 2.44436893e-05,
             8.29435941e-05, 2.50670786e-05, 4.22781901e-05, 5.70193132e-05,
             8.96077685e-05, 2.36816586e-04, 7.67799528e-05, 6.23427545e-06,
             4.58393676e-06, 7.18443693e-06, 7.87097983e-06, 7.57361167e-06,
             4.88274066e-06, 9.00127011e-05, 9.29402391e-04, 8.37179242e-04,
             4.12015094e-04, 6.52606163e-04, 4.13778009e-03, 1.80348034e-03,
             1.03274985e-03, 1.12324553e-03, 2.55541215e-03, 2.92800330e-03,
             3.66941342e-03, 4.63773320e-03, 6.74971157e-03, 3.11262206e-02,
             3.26273440e-02, 8.56047758e-02, 2.53505751e-01, 9.96320593e-02,
             2.39894248e-01, 2.21295658e-01, 5.28377288e-01, 5.10005861e-01,
             2.47290622e+00,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan])

    • ν
      (zeuc)
      float64
      nan nan nan nan ... nan nan nan nan

      long_name :

      ν

      units :

      W/kg

      standard_name :

      ocean_vertical_momentum_diffusivity

      array([           nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan, 4.31541293e-05, 1.87475808e-04, 1.60830071e-04,
             1.20034123e-04, 9.80373288e-05, 1.45866751e-04, 1.03845767e-04,
             1.00173097e-04, 8.12388520e-05, 7.24120615e-05, 5.18961109e-05,
             4.52475903e-05, 4.29103329e-05, 4.23408320e-05, 5.03857383e-05,
             7.92359774e-05, 9.48361491e-05, 8.97218275e-05, 6.08520635e-05,
             1.00874286e-04, 1.38767213e-04, 1.17102769e-04, 1.36578413e-04,
             2.22404547e-04, 3.77859350e-04, 6.89249517e-04, 1.10802370e-03,
             1.48866195e-03, 1.86612661e-03, 2.20730047e-03, 2.60012444e-03,
             3.17856744e-03, 3.98798150e-03, 4.76411095e-03, 6.05826586e-03,
             7.16354304e-03, 8.42592594e-03, 1.09242844e-02, 1.25466474e-02,
             1.57281395e-02, 1.80557653e-02, 2.37917973e-02, 3.90604785e-02,
             3.64038253e-02, 1.34412384e-01, 3.49902482e-02,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan,
                        nan,            nan,            nan,            nan])

  • Attributes: (3)

    starttime :

    ['Time:20:34:29 298 ' 'Time:20:42:18 298 ' 'Time:20:52:14 298 ' ... 'Time:18:56:50 313 ' 'Time:19:04:01 313 ' 'Time:19:11:46 313 ']

    endtime :

    ['Time:20:38:29 298 ' 'Time:20:46:29 298 ' 'Time:20:56:29 298 ' ... 'Time:19:01:00 313 ' 'Time:19:08:40 313 ' 'Time:19:16:00 313 ']

    name :

    EQUIX

• Dimensions:
• Coordinates: (0)
• Data variables: (0)
• Attributes: (0)

Timescales of comparison

Comparing a coarse climate model at 2/3° lateral grid spacing, and a time step of an hour, to microstructure observations that capture temperature variability at 7-15Hz is challenging.

The equator is a special place with strong diurnal variability in turbulence.

Capturing the daily timescale at the equator has been a vital benchmark for the KPP scheme (Large and Gent, 1999).

Interestingly we find that all simulations here reproduce a qualitatively similar variability at the daily frequency, even though variance is depressed at sub-daily and super-daily frequencies.

TODO: Frequency spectra comparison

Mean

Mean State Variable Profiles

1. A lot more shear, \(S, S^2\) just above the EUC when visc is turned down!

2. We are very slightly lower on \(S^2\), \(N_T^2\) in the top 80m, compare TAO, kpp.lmd.004, new_baseline.kpp.lmd.004.

3. Using the standard Ri_c=0.3, so deeper KPP surface layer, decreases \(S^2\), \(N_T^2\) in the top 60m. new_baseline.kpp.lmd.004 vs new_baseline.kpp.lmd.005

for context, Peters et al (1995) is interesting:

1. Variability patterns at 50-350 m are distinctly different from the upper 50 m containing the diurnal cycle of mixing

2. Large-scale shear of wavenumbers k < 0.01 cpm is associated with the slowly varying EUC and EIC. Large-scale is the dominant component of total shear above 50 m and in the thermostad, 170-270 m.

3. Fine-scale shear, 0.01cpm < k < 0.5 cpm,provides the dominant component of total rms shear and exceeds the large-scale component in thick layersaroundthe coresof EUC and EIC, where Fr < 1, at 50-170 m and below 270 m.

4. Variations of fine-scale shear cause variations in turbulent mixing; the large-scaleshear alone is a poor predictor of mixing.

5. Lacking a model that links large-scale, fine-scale, and turbulent flow components,our service to equatorial modelers consists of describing general levels of eddy diffusivities and their variability patterns.

These models should not be resolving “finescale” shear, and the mixing is not well correlated with “large-scale shear”, so we need a “background” diffusivity/viscosity to make things work.

But see that std(\(S^2\)) is a lot stronger above the EUC core, relative to TAO for new_baseline.kpp.lmd.004 and new_baseline.kpp.lmd.005

S2 = mixpods.plot_profile_fill(tree, "S2", "S^2")
N2 = mixpods.plot_profile_fill(tree, "N2T", "N_T^2")
u = mixpods.plot_profile_fill(tree, "sea_water_x_velocity", "u")
T = mixpods.plot_profile_fill(tree, "sea_water_potential_temperature", "T")
Ri = mixpods.plot_median_Ri(tree)

%autoreload

plot = (
    (S2 + N2 + Ri + u + T)
    .cols(5)
    .opts(
        hv.opts.Curve(xlim=(-350, 0), frame_height=600),
        hv.opts.Layout(toolbar="left"),
        hv.opts.Overlay(legend_offset=(0, 200)),
        clone=True,
    )
)
select_legends(plot, figure_index=4, legend_position="right")

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/holoviews/plotting/bokeh/plot.py:967: UserWarning: found multiple competing values for 'toolbar.active_drag' property; using the latest value
  layout_plot = gridplot(

A deeper look at mixing below the EUC

We find that tuning KPP’s shear scheme to better represent deep cycle turbulence by reducing \(Ri_0\) where median \(Ri_g\) is lower (0.25), prevents it from kicking in in the lower half of the EUC where median \(Ri_g\) is higher (1).

Microstructure observations from short term experiments (TIWE, EQUIX) do show higher viscosities \(ν \sim\) \SI{1e-4}{\meter\squared\per\second} below the EUC maximum.

We choose to preserve only TAO χpods above 90m depth, so there is very little data below the EUC core. For these data points, bins with less than 2000 hourly measurements are excluded.

With reduced \(Ri_0 \sim 0.5\), the KPP scheme is unable to reproduce these viscosities resulting a sharper EUC.

Peters et al (1995):

1. Fine-scale shear, 0.01cpm < k < 0.5 cpm,provides the dominant component of total rms shear and exceeds the large-scale component in thick layersaroundthe coresof EUC and EIC, where Fr < 1, at 50-170 m and below 270 m.

2. Variations of fine-scale shear cause variations in turbulent mixing; the large-scale shear alone is a poor predictor of mixing.

These simulations should not be resolving “finescale” shear, and the mixing is not well correlated with “large-scale shear”, so we need a “background” diffusivity/viscosity to make things work.

Indeed we see that additional background viscosity is necessary — compare new_baseline.hb to new_baseline.kpp.lmd.004.

tao_chi_counts = euc["TAO/chi"].count("time").load()
tao_chi_counts.hvplot.line(title="TAO χ number of hourly obs in bin")

euc_mean["TAO"] = euc_mean["TAO"].where(tao_chi_counts > 2000)

h = {
    varname: mixpods.map_hvplot(
        lambda node, name, muted: (
            node.ds.cf[varname]
            .reset_coords(drop=True)
            .hvplot.line(
                ylabel=varname,
                label=name,
                logx=varname != "sea_water_x_velocity",
                invert=True,
            )
        ),
        euc_mean,
    )
    for varname in [
        "sea_water_x_velocity",
        "sea_water_potential_temperature",
        "chi",
        "eps",
        "ocean_vertical_heat_diffusivity",
        "ocean_vertical_momentum_diffusivity",
    ]
}
h["chi"].opts(ylim=(1e-9, 1e-4))
h["eps"].opts(ylim=(1e-9, 1e-4))
h["ocean_vertical_heat_diffusivity"].opts(ylim=(5e-7, 3))
h["ocean_vertical_momentum_diffusivity"].opts(ylim=(1e-6, 1e-1))

h2 = {
    varname: mixpods.map_hvplot(
        lambda node, name, muted: node.ds[varname]
        .reset_coords(drop=True)
        .hvplot.line(ylabel=varname, label=name, logx=False, invert=True),
        euc_mean,
    )
    for varname in ["Rig_T"]
}

h

plot = (
    hv.Layout(list(h.values()) + [h2["Rig_T"].opts(ylim=(0, 3))])
    .opts(
        hv.opts.Curve(frame_width=150, frame_height=300, xlim=(-150, 150)),
        hv.opts.Layout(shared_axes=True),
        hv.opts.Overlay(show_legend=True, show_grid=True, legend_position="right"),
        *mixpods.HV_TOOLS_OPTIONS,
        *mixpods.PRESENTATION_OPTS,
    )
    .cols(4)
)
select_legends(plot, figure_index=3, legend_position="right")

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/holoviews/plotting/bokeh/plot.py:967: UserWarning: found multiple competing values for 'toolbar.active_drag' property; using the latest value
  layout_plot = gridplot(

Mean Turbulence Variable Profiles

mixpods.map_hvplot(
    lambda ds, name, muted: ds.ds.cf["ocean_vertical_heat_diffusivity"]
    .mean("time")
    .hvplot.line(label=name, muted=muted),
    tree,
).opts(
    ylim=(1e-8, 1e1),
    legend_position="right",
    logx=True,
    invert_axes=True,
    frame_width=300,
    aspect=1 / 3,
)

WARNING:param.OverlayPlot211169: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot211169: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.

WARNING:param.OverlayPlot211290: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot211290: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.

mixpods.map_hvplot(
    lambda ds, name, muted: ds["eps"]
    .mean("time")
    .load()
    .hvplot.line(label=name, muted=muted),
    tree,
).opts(
    ylim=(1e-10, None),
    legend_position="right",
    logx=True,
    invert_axes=True,
    frame_width=300,
    aspect=1 / 3,
)

WARNING:param.OverlayPlot212317: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot212317: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.

WARNING:param.OverlayPlot212438: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot212438: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.

mixpods.map_hvplot(
    lambda ds, name, muted: ds.ds.cf["ocean_vertical_heat_diffusivity"]
    .mean("time")
    .load()
    .hvplot.line(label=name, muted=muted),
    tree,
).opts(
    ylim=(1e-8, 1e1),
    legend_position="right",
    logx=True,
    invert_axes=True,
    frame_width=300,
    aspect=1 / 3,
)

WARNING:param.OverlayPlot213366: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot213366: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.

WARNING:param.OverlayPlot213467: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.
WARNING:param.OverlayPlot213467: aspect value was ignored because absolute width and height values were provided. Either supply explicit frame_width and frame_height to achieve desired aspect OR supply a combination of width or height and an aspect value.

Turbulence Variable distributions

handles = [
    mixpods.plot_distributions(tree, "chi", bins=np.linspace(-11, -4, 101), log=True),
    mixpods.plot_distributions(tree, "eps", bins=np.linspace(-11, -4, 101), log=True),
    mixpods.plot_distributions(
        tree, "ocean_vertical_heat_diffusivity", bins=np.linspace(-8, -1, 101), log=True
    ),
    # plot_distributions(tree, "Jq", bins=np.linspace(-1000, 0, 51), log=False),
    mixpods.plot_distributions(tree, "Rig_T", np.linspace(-0.5, 1.5, 61))
    * hv.VLine(0.25).opts(line_color="k"),
]

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:761: RuntimeWarning: invalid value encountered in log10
  result_data = func(*input_data)
/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/xarray/core/computation.py:761: RuntimeWarning: invalid value encountered in log10
  result_data = func(*input_data)

plot = (
    hv.Layout(handles)
    .opts("Overlay", frame_width=500)
    .cols(2)
    .opts(*mixpods.HV_TOOLS_OPTIONS)
)
select_legends(plot, figure_index=1, legend_position="right")

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/holoviews/plotting/bokeh/plot.py:967: UserWarning: found multiple competing values for 'toolbar.active_drag' property; using the latest value
  layout_plot = gridplot(

The daily timescale

Daily composites (Moum et al, 2023)

Hard to interpret! I think a lot of this is bias in KPP surface layer vs actively mixing layer in obs. We can write better diagnostics to check this (Moum et al, 2023)

1. The 89m χpod comparison is quite interesting. Suggests we do need more background visc

plot

%autoreload

plot = mixpods.plot_daily_composites(dailies, ["eps"], logy=True)
plot.opts(toolbar="left", show_legend=True)

%autoreload

mixpods.plot_daily_composites(
    dailies, ["S2", "N2", "Rig_T"], logy=False, legend=False
).opts(hv.opts.GridSpace(show_legend=True, width=200), hv.opts.Overlay(frame_width=200))

Daily composites: boundary layer depth

mixing_layer_depth_criteria = {
    "boundary_layer_depth": {"name": "KPPhbl|KPP_OBLdepth|ePBL_h_ML"},
}

hbl = (
    tree.drop_nodes("TAO")
    .dc.subset_nodes("KPP_OBLdepth")
    .dc.concatenate_nodes()
    .reset_coords(drop=True)
).load()

(
    # hbl.groupby("time.hour").mean().hvplot.line(by="node", flip_yaxis=True)
    hbl.groupby("time.hour")
    .mean()
    .hvplot.line(by="node", flip_yaxis=True)
    .opts(show_legend=False)
    + hbl.to_dataframe().hvplot.hist(
        by="node",
        bins=np.arange(0, 90, 1),
        normed=1,
        alpha=0.3,
        ylim=(0, 0.05),
        muted_alpha=0,
    )
).opts(
    hv.opts.Curve(invert_yaxis=True),
    *mixpods.HV_TOOLS_OPTIONS,
    *mixpods.PRESENTATION_OPTS,
)

Seasonal timescale

Seasonal mean profiles

Heat Budget

f, ax = plt.subplots(
    2,
    math.ceil(len(tree) / 2),
    constrained_layout=True,
    squeeze=False,
    sharex=True,
    sharey=True,
    figsize=(10, 3),
)

for axx, (name, node) in zip(ax.flat, tree.children.items()):
    mixpods.plot_climo_heat_budget_1d(node.ds, mxldepth=-40, penetration="mom", ax=axx)
    axx.set_title(name)

dcpy.plots.clean_axes(ax)

ENSO Timescale

S2, N2 histograms

Stability Diagram

1. ~Major change in La-Nina Warming for the new-baseline runs. We need to check ONI closely.~

   • fixed by just using the obs ONI everywhere.

     • This is probably OK since it shouldn’t drift too far away.

     • And we now average over the same time periods.

   • It is a pain to estimate this with branch runs,

   • but could be done if we spliced in the SST from the baseline case

2. I still can’t reproduce the Warner and Moum (2019) figure where the El-Nino warming phase overlaps a lot less with La-Nina cooling

• I’m only using data in the “deep cycle layer”.

  • have not matched vertical grid spacing yet.

  • could interpolate to TAO χpod depths

• think about resampling to daily. Currently hourly.

• think about instrumental error on vertical gradients for S2, N2;

  • if added to model, would it change the bottom tail.

%autoreload

mixpods.plot_stability_diagram_by_dataset(tree, nrows=2)

ε-Ri histograms

%autoreload

mixpods.map_hvplot(
    lambda dt, name, muted: mixpods.plot_eps_ri_hist(
        dt["eps_ri"].load(), label=name, muted=muted
    ),
    tree.children,
).opts(
    hv.opts.Curve(ylim=(1e-8, 3e-6)),
    hv.opts.GridSpace(show_legend=True),
    hv.opts.Overlay(show_grid=True, legend_position="bottom_left"),
    *mixpods.HV_TOOLS_OPTIONS,
)

/glade/u/home/dcherian/miniconda3/envs/pump/lib/python3.10/site-packages/holoviews/plotting/bokeh/plot.py:588: UserWarning: found multiple competing values for 'toolbar.active_drag' property; using the latest value
  plot = gridplot(plots[::-1],